text:
\section{Introduction}

Biometric data such as face, fingerprint or iris images reveal information about the identity of the individual as well as the identity of the device used to acquire the data~\cite{BiomSensor_1, BiomSensor_2}. In some applications such as smartphone banking, it is necessary to authenticate both the {\em user} as well as the {\em device} in order to enhance security~\cite{Galdi_PRL_15, Ross_ICB_18}. This can be done by invoking two separate modules: one for biometric recognition and the other for device or sensor recognition.\footnote{The terms ``device" and ``sensor" are interchangeably used in this paper. Thus, determining the identity of a smartphone camera (\textit{i.e.,} sensor) is akin to determining the identity of the smartphone (\textit{i.e.,} device) itself.} In such cases, the system has to store two distinct templates: a biometric template denoting the identity of the user and sensor template denoting the identity of the device.

In this paper, we approach this problem by designing a {\em joint template} that can be used to authenticate both the user and the device simultaneously. Our objective is as follows: \textit{Given a biometric image we would like to simultaneously recognize the individual and the acquisition device}. In the process of accomplishing this objective, we address the following questions:

\begin{figure}[t]
\centering
\subfloat[]
{
    \includegraphics[scale=.37]{FIG1_NEW}
}\\
\subfloat[]
{
    \includegraphics[scale=.37]{FIG1_NEWB.png}
}

\caption{Difference between (a) methods that use separate modules for computing biometric and sensor representations, and (b) the proposed method that uses an embedding network to generate a joint biometric-sensor representation. }
\label{Fig: Obj}
\end{figure}
\begin{enumerate}
\item \textbf{Why do we need to combine biometric and device recognition?} \\
Smartphones are increasingly using biometrics for access control and monetary transactions. Examples include fingerprint and face recognition on iPhones and iris recognition on Samsung Galaxy S9. Device verification\footnote{Typically used in two factor authentication (2FA)  protocol that combines any two of the three factors: `something you are' (biometrics), `something you have' (a code on the authorized device) and `something you know' (a password) for additional security.} can provide assurance that the biometric sample is being acquired by an authorized device. A combined biometric and device recognition system can therefore guarantee that the right person is accessing the remote service (\textit{e.g.}, banking) using an authorized device.

\item \textbf{Can existing device verification techniques be used in the smartphone application scenario?} \\
Device identification can be performed using the MAC (media access control) address, a unique networking address assigned to each device. However, in case of smartphones that have multiple network interfaces, such as Wi-Fi, 4G, bluetooth, etc., there can be multiple MAC addresses which may be broadcasted, making them vulnerable. Alternatively, SRAM cells can be used to deduce physically unclonable cues for device identification~\cite{Ross_ICB_18}; this is a hardware-based solution and requires access to the physical device. In a mobile banking scenario, where the verification is conducted remotely, the customer provides a biometric sample in the form of an image, and some device information, but not necessarily the physical device itself. In this scenario, hardware-based solutions will be ineffective.

\item \textbf{Why do we need a joint representation?} \\
Existing literature uses separate modules to tease out the biometric-specific and sensor-specific details from an image and perform feature-level or score-level fusion~\cite{Galdi_PRL_15, Ross_ICB_18}. However, they suffer from the following limitations: (i) the overall performance is limited by the weakest recognition module, and (ii) the process may not generalize well across different biometric modalities and multi-spectral sensors. Therefore, a \textit{joint} representation that combines both biometric and sensor-specific features present in a biometric image can offer the following advantages: (i) the joint representation is not constrained by the performance of the individual recognition module, and the same \textit{method} can be employed across different biometric modalities, and (ii) the joint representation integrates the biometric and sensor representations into a compact template, such that, the  individual templates cannot be easily de-coupled; this implicitly imparts privacy to the biometric component.

\end{enumerate}

\section{Related Work}

Biometric recognition systems comprise a \textit{feature extraction} module that elicits a salient feature representation from the acquired biometric data, and a \textit{comparator} module that compares two sets of feature representations to compute a match score~\cite{Jain_Intro}. On the other hand, sensor recognition systems extract sensor pattern noise~\cite{Lukas_TIFS_06} from a set of training images obtained from different sensors to generate \textit{sensor reference patterns}. To deduce the sensor identity of an unknown test image, first its sensor pattern noise is extracted, and then it is \textit{correlated} with the reference patterns. The test image is assigned to the device whose reference pattern yields the highest correlation value.

In~\cite{Galdi_ICIAP}, the authors used partial face images acquired using smartphones and employed a weighted sum fusion rule at the score level to combine sensor and biometric recognition. Later, they extended their work to include feature level fusion in~\cite{Galdi_PRL_15} and concluded that score level fusion performed comparatively better. In~\cite{Galdi_Face}, the authors performed HOG-based face recognition and combined it with Photo Response Non-Uniformity-based sensor recognition at the score level. In~\cite{Ross_ICB_18}, the authors combined fingerprint recognition with device recognition by performing feature level fusion of minutiae-cylinder-codes with SRAM start-up values. Fusion at the score or feature level is often dependent on the specific biometric modality and the device sensor used. A specific fusion rule producing the best results on a particular biometric and sensor modality (\textit{e.g,} iris and near-infrared sensors) may not yield optimal results on a different modality (\textit{e.g,} face and RGB sensors), and therefore, needs to be tuned separately for each pair of biometric and sensor modalities. Furthermore, feature-level fusion retains the individual biometric and sensor-specific components that can be recovered from the fused representation using appropriate measures. Obtaining the biometric template may compromise the privacy aspect of biometrics. In contrast, the proposed joint representation non-trivially unifies the biometric and sensor-specific features. As a result, typical countermeasures will be ineffective in disentangling the biometric component from the joint representation. This will implicitly preserve the privacy of the biometric component.

The remainder of the paper is organized as follows. Section~\ref{Sec:Prop} describes the proposed method. Section~\ref{Sec:DSandExp} describes the dataset and experimental protocols used in this work. Section~\ref{Sec:Res} reports the results. Section~\ref{Sec:Sum} summarizes the findings and concludes the paper.

\section{Motivation and Proposed Method}
\label{Sec:Prop}
An image contains both low frequency and high frequency components. For example, in a face image, the low frequency components capture the illumination details while the high frequency components capture the structural details present in the face that are useful for biometric recognition. Recently, sensor recognition has been successully accomplished using Photo Response Non-Uniformity (PRNU)~\cite{Lukas_TIFS_06} for different types of sensors, such as DSLR sensors~\cite{Chen_TIFS_08}, smartphone sensors~\cite{BTAS_19}, and also near-infrared iris sensors~\cite{Uhl4_ICB_12}. PRNU is a form of sensor pattern noise in an image that manifests due to anomalies during the fabrication process and is, therefore, unique to each sensor. Typically, PRNU resides in the high frequencies that can be useful for sensor recognition~\cite{Lukas_TIFS_06}. Since the high frequencies dominate in both biometric and sensor representations, we hypothesize that there is a joint representation, that, if effectively extracted, can be utilized for both tasks of biometric and sensor recognition. Our objective  is to \textit{learn} this joint representation that lies at the intersection of the sensor and biometric space. Mathematically, it can be represented as
$\displaystyle J(\bm{X}) = B(\bm{X}) \cap S(\bm{X})$, where $\bm{X}$ is an input biometric image, $B(\cdot)$ is the biometric representation extracted from $\bm{X}$, $S(\cdot)$ is the sensor representation computed from the same input $\bm{X}$, and $J(\cdot)$ is the joint representation. Existing methods process $\bm{X}$ using two independent routines to extract the two representations, and can optionally perform fusion, either at feature level or at score level, to make a decision. However, we propose to leverage the two representations to derive a joint representation (see Figure~\ref{Fig: Obj}). The joint space can be best approximated using an embedding network that can convert images to compact representations~\cite{Emb1}. The embedding network  $\mathcal{E}$, takes two inputs, $\bm{X}$ and the dimensionality ($k$) of the embedding to be generated, such that $\displaystyle  J(\bm{X}) = \mathcal{E}(\bm{X},k) \approx B(\bm{X}) \cap S(\bm{X})$. The second argument $k$, allows us to regulate the dimensionality of the joint representation, which will be much lesser than the original dimensionality of the image, as well as the combined dimensionality of the two representations computed separately, \textit{i.e.}, if $\bm{X} \in \mathbb{R}^d$, $B(\bm{X}) \in \mathbb{R}^m$ and $S(\bm{X}) \in \mathbb{R}^n$, then the joint representation $J(\bm{X}) \in \mathbb{R}^k$, where, $k << d$ and $k < (m+n)$.

\begin{figure*}[h]
\centering

    \includegraphics[scale=.39]{Fig_outline_CR}

\caption{Outline of the proposed method used for computing the joint biometric and sensor representation. \textbf{Input:} A single image, or a pair of images, or 3-tuple images to the embedding network. \textbf{Output:} Joint biometric-sensor representation. The embedding network is trained in three mutually exclusive modes, \textit{viz.,} classical mode (top row), siamese mode (middle row) and triplet mode (bottom row). The switching circuit selects only one training mode at a time.}
\label{Fig: Prop}
\end{figure*}

In this work, we used a deep convolutional neural network that serves the role of the embedding network (see Figure~\ref{Fig: Prop}). The embedding network consists of two 2-D convolutional layers and three linear layers. We used max-pooling for down-sampling the feature map and a parametric-rectified linear activation unit (PReLU) as the activation function. The embedder accepts an image, resized to $48 \times 48$, as the input and produces a 8-dimensional output, which is the \textit{joint} representation. The choice of the dimensionality of the representation along with the experimental setup is described later (see Section~\ref{Sec: Expts}).

The \textbf{main contributions} of this work are as follows:
\begin{enumerate}
\item We propose a method to learn a joint biometric and sensor representation using a one-shot approach that can be used in joint identification and joint verification scenarios. A correct joint identification/verification occurs \textit{only} if both subject identity and device identity yield correct matches.
\item We employ an embedding network that can learn the joint representation irrespective of the biometric modality and the sensor used for acquisition. In this context, we evaluate the proposed method using three different biometric modalities (face, iris and periocular), and different types of sensors (iris sensors operating in the near-infrared spectrum and smartphone camera sensors operating in the visible spectrum).
\item We perform extensive experiments using different training paradigms and loss functions, and compare the proposed method with existing state-of-the-art algorithms for biometric and sensor recognition.
\end{enumerate}

\section{Datasets and Experiments}
\label{Sec:DSandExp}
\subsection{Datasets}

In this work, we focused on three different biometric modalities, \textit{viz}., iris, periocular and face. To this end, we used three different datasets - (i) CASIA-Iris Image Database Version 2~\cite{CasV2} which contains near-infrared iris images acquired using two sensors, (ii) Mobile Iris Challenge Evaluation (MICHE-I) dataset~\cite{MICHE} which contains partial face images acquired using two smartphones (front and rear sensors separately) and front camera of a tablet, and (iii) OULU-NPU dataset~\cite{OULU} which contains face images acquired using the front sensors of six smartphones. We used \textit{only} bonafide images from all three sessions in the OULU-NPU dataset. Table~\ref{Tab: Dataset} describes the datasets used in this work. Note that the smartphone datasets (MICHE-I and OULU-NPU) contain images acquired in the visible spectrum.

\begin{table*}[]
\caption{Dataset specifications used in this work. We used three datasets corresponding to 3 biometric modalities \textit{viz.,} iris, periocular and face. Here, we perform joint biometric and sensor recognition, so total $\# Classes$ is computed as the product of $\# Subjects$ and $\# Sensors$. ($^*$MICHE-I dataset has a total 75 subjects, out of which the first 48 subjects were imaged using iPhone 5S UNIT I and the remaining 27 subjects were imaged using iPhone 5S UNIT II, as observed in~\cite{Galdi_PRL_15}. Here, `UNIT' refers to two different units of the same brand and model iPhone 5S, and therefore, should be treated as two different smartphones. In this case, $\# Classes$ = 375 since only a subset of the total 75 subjects were imaged using either of the two units of iPhone 5S smartphone at a time. Therefore, $75 \times 3$ [Samsung] $+ 48 \times 2$ [UNIT I] $+ 27 \times 2$ [UNIT II] $= 375$.) }
\label{Tab: Dataset}
\begin{tabular}{|l|ll|l|l|l|} \hline
\textbf{Modality}           & \textbf{Dataset}               & \textbf{\begin{tabular}[c]{@{}l@{}}Name of sensors\end{tabular}}                                                                                & \textbf{\begin{tabular}[c]{@{}l@{}}(\# Subjects, \# Sensors, \\ \# Classes)\end{tabular}} & \textbf{Split} & \textbf{\# Images} \\ \hline \hline
\multirow{2}{*}{Iris}       & \multirow{2}{*}{CASIA-Iris V2} & \multirow{2}{*}{CASIA IrisCAM-V2, OKI IrisPass-h}                                                                                & \multirow{2}{*}{(60, 2, 120)}                                                             & Train          & 1,680              \\
                            &                                &                                                                                &                                                                                & Test           & 720                \\ \hline
\multirow{2}{*}{Periocular} & \multirow{2}{*}{MICHE-I}       & \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}Apple iPhone 5S (Front and Rear) UNIT I and UNIT II, \\ Samsung Galaxy S4 (Front and Rear), Samsung Galxy Tab GT2 (Front)\end{tabular}}     & \multirow{2}{*}{(75, 7, 375$^*$)}                                                             & Train          & 2,278              \\
                            &                                &                                                                                &                                                                                & Test           & 863                \\ \hline
\multirow{2}{*}{Face}       & \multirow{2}{*}{OULU-NPU}      & \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}HTC Desire EYE, Sony XPERIA C5 Ultra Dual, MEIZU X5,\\ Oppo N3, Samsung Galaxy S6 Edge, ASUS Zenfne Selfie\end{tabular}} & \multirow{2}{*}{(55, 6, 330)}                                                             & Train          & 5,940              \\
                            &                                &                                                                                &                                                                                & Test           & 2,970              \\ \hline \hline
\multicolumn{3}{|l|}{TOTAL}                                                                                & (190, 15, 825)                                                                            &                & 14,451            \\ \hline
\end{tabular}
\end{table*}

\subsection{Evaluation Protocol}
\label{Sec: EvalProto}
Before we describe the experiments, we present the protocol that is used to evaluate the proposed approach. We evaluate the method in two scenarios, \textit{viz.,} (i) joint identification and (ii) joint verification. The terms joint identification and joint verification are different from the terms used conventionally in the biometric literature. In the case of \textbf{joint identification}, a correct identification occurs \textit{only} when both sensor and subject labels of the test sample match with the ground truth labels. To perform evaluation in the joint identification scenario, we select one embedding from each class (combines both sensor and subject label) to form the gallery, and the remaining embeddings are used as probes. We use two metrics to compute the distance or similarity between the probe and gallery embeddings and select the top three matches: (i) standardized Euclidean distance (computes the pairwise euclidean distance divided by the standard deviation) and (ii) cosine similarity. We plot the cumulative match characteristics (CMC) curves corresponding to the top three ranks. In the case of \textbf{joint verification}, two joint representations will yield a match if both the embeddings belong to the same sensor and same subject, otherwise a mismatch occurs. Incorrect match can occur in three cases as follows: (i) if the two joint representations belong to the same subject, but different sensors, (ii) if the two joint representations belong to the same sensor, but different subjects, and (iii) if the two joint representations belong to different subjects and different sensors. To perform evaluation in the joint verification scenario, we compute the distance or similarity between all the test embeddings and present receiver operating characteristics (ROC) curves to indicate the joint verification performance. We also report the true match rate (TMR) values @1\% and 5\% false match rates (FMR).

\subsection{Experimental Settings}
\label{Sec: Expts}

In this work, we designed the experimental settings using three different modes of training. See Figure~\ref{Fig: Prop}. Say, $\bm{O}$ denotes the output of an embedding network for input $\bm{X}$, \textit{i.e.}, $\bm{O} = \mathcal{E}(\bm{X},k)$. In the first mode, referred to as the \textit{classical} mode, the embedding $\bm{O}$ is fed to a classification network which minimizes the cross-entropy loss computed between the ground truth label and the predicted label. The classification network in our case is a shallow network which applies PReLU activation on the embedding, followed by a fully-connected layer and then applies softmax to compute a probability value. We assigned the ground truth label for the $i^{th}$ image, such that $l_i \in Sub_i \otimes\ Sen_i$, where $Sub_i$ denotes the subject identifier of image $i$, $Sen_i$ denotes the sensor identifier for the same image and $\otimes$ denotes the tensor product. The cardinality of the set of labels $|L| = |Sub \times Sen|$. In the second mode, referred to as the \textit{siamese} mode, a siamese network~\cite{siamese} is used which feeds a pair of images to the embedding network. The embedding network then computes a pair of embeddings ($\bm{O}_i, \bm{O}_j$) and the siamese network is trained by minimizing the contrastive loss~\cite{contrastive} computed between the pair of embeddings. We used single margin (SMCL) and double margin (DMCL) contrastive losses. Finally, in the third mode, referred to as the \textit{triplet} mode, a triplet network~\cite{tripletnetwork} is trained using embeddings generated from an anchor ($\bm{O}_a$), a positive ($\bm{O}_p$) and a negative ($\bm{O}_n$) sample by minimizing the triplet loss~\cite{tripletloss}. We performed offline triplet mining as well as online triplet mining~\cite{TLmining} with different triplet selection strategies (random negative triplet selection, semi hard negative triplet selection and hardest negative triplet selection). The triplet loss considers only one negative example at a time. Alternatively, multi-class N-pair loss function~\cite{npair} considers multiple negative instances from several classes. In this work, we consider a positive example as one which belongs to the same class as the anchor (same subject and same sensor), whereas there can be three types of negative examples, \textit{viz}., same subject but different sensor, same sensor but different subject and different sensor with different subject. Therefore, the number of negative classes in this work is significantly high, so we used multi-class N-pair loss using two mining techniques: (i) all positive pairs and (ii) hard negative pairs. Table~\ref{Tab: Loss} summarizes the different loss functions used in the three training modes in this work. Note that each input to the embedding network as shown in Figure~\ref{Fig: Prop} is \textbf{mutually exclusive}, \textit{i.e.}, the embedding network can operate independently in any of the three training modes. We modified the design of an existing embedding network for implementing the different training paradigms~\cite{imp}. We used learning rate = $1\times \exp{(-4)}$, batch size = 4, Adam optimizer, and a step decay to reduce the learning rate by a factor $\gamma = 0.1$ every 8 epochs. The proposed network is shallow so we trained only for 50 epochs. The margin values in single margin contrastive loss and triplet losses are set to 1, while in double margin contrastive loss, both margins are set to 0.5.

\begin{table}[]
\centering
\caption{Description of the training modes and the loss functions used in this work.}
\label{Tab: Loss}
\scalebox{1.1}{
\begin{tabular}{|l|l|l|} \hline
\multicolumn{1}{|c|}{\textbf{Training mode}} & \multicolumn{2}{c|}{\textbf{Loss function}}                  \\  \hline \hline
Classical                                  & \multicolumn{2}{c|}{Cross entropy}                           \\ \hline
\multirow{2}{*}{Siamese}                   & \multicolumn{2}{c|}{Single margin contrastive loss (SMCL)}          \\
                                           & \multicolumn{2}{c|}{Double margin contrastive loss (DMCL)}          \\ \hline
\multirow{6}{*}{Triplet}                   & \multicolumn{2}{c|}{Offline triplet mining}                  \\ \cline{2-3}
                                           & \multirow{3}{*}{Online triplet mining} & Random negative    \\
                                           &                                        & Semi-hard negative \\
                                           &                                        & Hardest negative   \\ \cline{2-3}
                                           & \multirow{2}{*}{Multi-class N-pair}    & All positive pair  \\
                                           &                                        & Hard negative pair \\ \hline
\end{tabular}}
\end{table}

For each dataset, we used a training set and a test set (see Table~\ref{Tab: Dataset}). The number of classes is computed as the product of the number of sensors and number of subjects in that dataset. For example, CASIA-Iris V2 dataset has 60 subjects and 2 sensors, so total number of classes is $60 \times 2=120$. Each class has 20 images, therefore, the total number of images (samples) is 2,400 ($20 \times 120$). The training and test partitions follows a 70:30 split. So, for a single class, out of 20 samples, 14 samples are randomly selected as the training set and the remaining 6 samples form the test set. Similar protocol is followed for the remaining datasets.

\begin{figure}[h]
\centering

    \includegraphics[scale=.27]{dim_fig.eps}

\caption{Variation in the joint verification performance as a function of the dimensionality of the joint representation. Experiment is conducted on the validation set using 50 images from the MICHE-I dataset and four dimensionality values \textit{viz.,} $\{4,8,16,32\}$. 8-dimensional embedding resulted in the highest joint verification accuracy, and is therefore selected in this work.}
\label{Fig: Dim}
\end{figure}

Next, in the training phase, the embedding network accepts an image (resized to $48 \times 48$) as input. Different image resolutions were used $\{28 \times 28, 48 \times 48, 96 \times 96 \}$, but $48 \times 48$ provided optimal trade-off between accuracy and training time. The embeddings are trained in (i) classical, (ii) siamese and (iii) triplet modes. Then, in the testing phase, we computed the embeddings from the test set. We evaluate the test embeddings in joint identification and joint verification scenarios.

Although deep learning-based sensor identification methods exist in the literature~\cite{CR_Ref1, CR_Ref2, CR_Ref3}, we used Enhanced PRNU~\cite{Li_TIFS_10} (with enhancement Model III) as the sensor identification baseline for all three modalities due to its low computational burden and effectiveness against multi-spectral images~\cite{Ross_18}. Enhanced PRNU requires creation of sensor reference patterns, that serve as gallery and test (probe) noise residuals, that are correlated with the reference patterns. We used training images to compute the sensor reference patterns and test images for correlation. A test image is assigned to the sensor class resulting in the highest correlation value. See~\cite{BTAS_19} for more details. Test noise residuals computed from JPEG images can be matched successfully against sensor reference patterns computed from RAW images~\cite{PRNU_JPEG}, thereby, justifying the use of PRNU as a state-of-the-art sensor identification baseline. We used COTS matcher as the biometric recognition baseline for iris and face modalities. For the periocular modality, we used a pretrained ResNet-101 architecture~\cite{ResNet} and used the features from layer 170 as the biometric representation for the test samples. This particular architecture is used because it has demonstrated good performance in biometric verification on the MICHE-I dataset~\cite{BTAS_19}. The gallery comprises the training images and the probes are the test images. Since, PRNU can only be used for the task of sensor identification, we selected to implement both the baselines only in identification scenario.

We further conducted an experiment using a validation set comprising 50 images from the MICHE-I dataset (excluded from the test set) to analyze the effect of the dimensionality of the embedding on the verification performance. To this end, we used four values of $k = \{4,8,16,32\}$, and then selected that value which results in the highest performance for the remaining experiments.

\begin{figure}[]
\centering

    \includegraphics[scale=.27]{sensortsne}

\caption{2-D projection of the embeddings using t-SNE used for sensor identification in the OULU-NPU dataset. Each sensor class is sufficiently discriminated from the rest of the sensors.}
\label{Fig: tsne}
\end{figure}

\section{Results and Analysis}
\label{Sec:Res}

\subsection{Selection of the metric and dimensionality of embedding}

In terms of the choice of the distance/similarity \textbf{metric}, we observed that standardized euclidean distance metric resulted in better performance compared to the cosine similarity metric. This can be attributed to the standardization process which takes into account the intra-class and inter-class variations in the embeddings. In terms of the choice of the \textbf{dimensionality} of the embedding, we observed that 8 was the optimal value, since, it resulted in the best performance (64\% on the MICHE-I validation set) as indicated in Figure~\ref{Fig: Dim}. Therefore, we used 8-dimensional embedding and standardized Euclidean distance metric for all the experiments. Furthermore, we presented the t-SNE~\cite{TSNE} visualization of the performance of the embedding network in terms of sensor identification for the OULU-NPU dataset in Figure~\ref{Fig: tsne}. The well-separable clusters corresponding to the six sensors demonstrate the capability of the embedding network used in this work.

\subsection{Performance of each of the three training modes}

In terms of \textbf{training algorithms}, the overall results in both joint identification and joint verification scenarios indicate that the embedding network trained in \textit{siamese} mode outperformed the remaining training paradigms (see Figures~\ref{Fig: CMC} and~\ref{Fig: ROC}). The reason for the superior performance of siamese network can be attributed to the use of contrastive loss. Out of the two contrastive losses, single margin contrastive loss outperformed double margin contrastive loss. The contrastive loss considers a pair of embeddings at a time, and tries to either minimize the distance between them if they belong to the same class, or increases the distance between them by some margin if they belong to different classes. On the other hand, triplet loss tries to \textit{simultaneously} minimize the distance between the anchor and positive sample, whereas, maximize the distance between the anchor and negative samples. In this work, the number of negative classes is very high (in a 330 class dataset, 1 class is positive and the remaining 329 classes are negative). This makes the task of triplet loss much more complex as compared to contrastive loss. Given the huge variation in the possible combination of negative triplets (see Figure~\ref{Fig: Prop}), we suspect that the triplet loss struggled to determine the accurate decision boundary between the positive and negative classes, resulting in an overall reduction in performance. We investigated different types of triplet mining strategies, and observed that online triplet mining outperformed offline triplet mining and multi-class N-pair in a majority of the cases.

\begin{table}[]
\centering
\caption{Results in the joint identification scenario. Results are reported in terms of Rank 1 identification accuracies (\%). A correct joint identification implies that \textit{both} sensor and subject resulted in a match. Mismatch of either subject or sensor or both will result in an incorrect joint identification.}
\label{Tab: Ident}
\scalebox{0.8}{
\begin{tabular}{|l|l|l|l|l|l|} \hline
\multirow{2}{*}{Dataset} & \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}Method for \\ baseline\end{tabular}} & \multicolumn{3}{c|}{Baseline performance}                                                                                & \multirow{2}{*}{\begin{tabular}[c]{@{}l@{}}Proposed \\ method \\ (\%)\end{tabular}} \\ \cline{3-5}
                         &                                                                                & \begin{tabular}[c]{@{}l@{}}Sensor\\ identification \\ (\%) \end{tabular} & \begin{tabular}[c]{@{}l@{}}Biometric\\ identification \\ (\%)\end{tabular} & \begin{tabular}[c]{@{}l@{}}Joint\\ identification \\ (\%)\end{tabular} &                                                                                \\ \hline \hline
\begin{tabular}[c]{@{}l@{}}CASIA- \\Iris V2 \end{tabular}            & \begin{tabular}[c]{@{}l@{}}PRNU \\COTS \end{tabular}  & 100.00                                                          & 56.52                                                            & 56.52                                                          & \textbf{89.67 } \\ \hline
MICHE-I                  & \begin{tabular}[c]{@{}l@{}}PRNU \\ResNet-101 \end{tabular}  & 99.86                                                           & 18.05                                                            & 18.05                                                          & \textbf{47.53                                                                                } \\ \hline
OULU-NPU                 & \begin{tabular}[c]{@{}l@{}}PRNU \\COTS \end{tabular}  & 98.48                                                           & 84.24                                                            & 83.13                                                        & \textbf{99.81 } \\ \hline
\end{tabular}}
\end{table}

\subsection{Results of the joint identification experiment}
In terms of the performance in \textbf{joint identification scenario}, Table~\ref{Tab: Ident} compares the results with the baseline performance for all the datasets. We reported the baselines for sensor identification (PRNU), biometric identification (COTS or ResNet), followed by joint identification, separately. We reiterate that joint identification involves a correct match only if both sensor and subject labels are correct to allow fair comparison with the proposed method. Results indicate that the proposed method outperformed the baseline (joint identification) by 26.41\% averaged across all three datasets computed at Rank 1. The poor performance for the MICHE-I dataset can be attributed to two factors - firstly, the large number of classes (= 375) compared to rest of the datasets (see Table~\ref{Tab: Dataset}), and secondly, the diverse acquisition settings (indoor vs. outdoor) resulting in degraded biometric recognition, and subsequently leading to overall poor performance. Surprisingly, the proposed method can still outperform the baseline by $\sim$30\%. We have further analyzed this performance in Section~\ref{MICHE_Analysis}. CMC curves indicate the superior performance of the siamese network in contrast to classical and triplet networks.

\begin{figure*}[h]
\centering
\subfloat[]
{
    \includegraphics[scale=.3]{CMC_SEUC_CASIA_CR}
} \hspace{-0.75cm}
\subfloat[]
{
    \includegraphics[scale=.3]{CMC_MICHELEFTANDRIGHT_SEUC_CR}
} \hspace{-0.75cm}
\subfloat[]
{
    \includegraphics[scale=.3]{CMC_SEUC_OULU_CR}
}

\caption{Cumulative Matching Characteristics (CMC) curves for the proposed method in the \textbf{joint identification scenario} for the following datasets: (a) CASIA-Iris V2 (b) MICHE-I and (c) OULU-NPU. Refer to Table~\ref{Tab: Loss} for the different training networks and loss functions indicated in the legend in an identical order.}
\label{Fig: CMC}
\end{figure*}

\begin{figure*}[h]
\centering
\subfloat[]
{
    \includegraphics[scale=.315]{ROC_SEUC_CASIA_CR}
} \hspace{-0.75cm}
\subfloat[]
{
    \includegraphics[scale=.315]{ROC_MICHELEFTANDRIGHT_SEUC_CR}
}\hspace{-0.75cm}
\subfloat[]
{
    \includegraphics[scale=.315]{ROC_SEUC_OULU_CR}
}

\caption{Receiver Operating Characteristics (ROC) curves for the proposed method in the \textbf{joint verification scenario} for the following datasets: (a) CASIA-Iris V2 (b) MICHE-I and (c) OULU-NPU. Refer to Table~\ref{Tab: Loss} for the different training networks and loss functions indicated in the legend in an identical order. }
\label{Fig: ROC}
\end{figure*}

\begin{table}[]
\centering
\caption{Results in the joint verification scenario. Results are reported in terms of true match rate (TMR) at false match rates (FMRs) of 1\% and 5\%.}
\label{Tab: Veri}
\scalebox{1.2}{
\begin{tabular}{|l|l|l|} \hline
Dataset       & TMR@FMR=1\% & TMR@FMR=5\% \\ \hline \hline
CASIA-Iris V2 & 90.00     & 98.00     \\ \hline
MICHE-I       & 62.00     & 90.00     \\ \hline
OULU-NPU      & 100.00    & 100.00 \\ \hline
\end{tabular}}
\end{table}

\subsection{Results of the joint verification experiment}
In terms of the performance in \textbf{joint verification scenario}, Table~\ref{Tab: Veri} reports the results. Results indicate that the proposed method achieved an average joint TMR of 84\% @1\% FMR, and an average TMR of 96\%  @5\% FMR, indicating the strong representative capability of the joint representation. ROC curves in Figure~\ref{Fig: ROC} indicate that the joint representation learnt using siamese network trained with single margin contrastive loss (see the curve marked Siamese-SMCL-Emb[Joint]) outperformed the remaining joint representations. We would like to point out that in~\cite{Galdi_PRL_15}, the authors achieved 23\% (by using feature level fusion) and 86\% (by using score level fusion) at 5\% FMR on the MICHE-I dataset (the authors excluded the Samsung Galaxy Tab 2 subset of the MICHE-I dataset, which we included in our evaluations). Although their objectives were different compared to the proposed work (they adopted a fusion rule for integrating their proposed biometric and sensor recognition performances), we would like to indicate that the task of joint recognition is difficult. In spite of that, the proposed method performed reasonably well.

\subsection{Analysis of the performance of the proposed method on MICHE-I dataset}
\label{MICHE_Analysis}
In both cases of joint identification and joint verification experiments, we observed that the performance of the proposed method evaluated on the MICHE-I dataset was relatively worse compared to the remaining two datasets. We hypothesize that the poor performance can be attributed to two reasons: (i) the image characteristics, and (ii) the variation in the performance across different lateralities, \textit{i.e.,} left vs. right periocular images. MICHE-I dataset was assembled as a part of an iris challenge evaluation and contains images acquired in unconstrained settings (indoor and outdoor settings) having occlusions (specular reflection and downward gaze). See some challenging images from the MICHE-I dataset images in Figure~\ref{Fig: MICHE}. In contrast, CASIA and OULU datasets contain images acquired in controlled settings.

\begin{figure}[h]
\centering
\subfloat[]
{
    \includegraphics[scale=.07]{OCCL_1}
}\hspace{0.3cm}
\subfloat[]
{
    \includegraphics[scale=.03]{SPEC1}
}\hspace{0.3cm}
\subfloat[]
{
    \includegraphics[scale=.03]{BACK}
}\hspace{0.3cm}
\subfloat[]
{
    \includegraphics[scale=.07]{RIGHTEYE}
}

\caption{Example images from the challenging MICHE-I dataset. (a) Occlusion, (b) Downward gaze and specular reflection, (c) Prominent background in the outdoor setting and (d) A single image containing both eyes but labeled as right eye image (061\textunderscore GT2\textunderscore OU\textunderscore F\textunderscore RI\textunderscore 01\textunderscore 3 where, RI indicates right eye).}
\label{Fig: MICHE}
\end{figure}

We presented the CMC curves corresponding to joint identification results for two lateralities separately in Figure~\ref{Fig: CMCMICHE}. Results indicate that the proposed method performed better on left periocular images compared to right periocular images. This variation in the performance across the two lateralities resulted in the overall poor performance on the entire MICHE-I dataset. MICHE-I dataset has an imbalanced distribution of lateralities. Only 30\% of the total number of images are of left periocular images. We hypothesize that the imbalanced distribution coupled with some mislabeled test case (see Figure~\ref{Fig: MICHE}(d)) may have further compounded the challenges, resulting in overall poor performance.

\begin{figure}[h]
\centering
\subfloat[]
{
    \includegraphics[scale=.3]{CMC_MICHE_LEFTONLY_CR}
} \hspace{-0.75cm}
\subfloat[]
{
    \includegraphics[scale=.3]{CMC_MICHE_RIGHTONLY_CR}
}

\caption{Cumulative Matching Characteristics (CMC) curves for the proposed method in the joint identification scenario for the MICHE-I dataset evaluated separately on the two lateralities. (a) Left periocular images and (b) Right periocular images. Results indicate that the proposed method performs better on left periocular images compared to right periocular images.}
\label{Fig: CMCMICHE}
\end{figure}

The \textbf{main findings} from the experiments are as follows:
\begin{enumerate}
\item The joint biometric and sensor representation performed well in both joint identification scenario, with an average identification accuracy of $\sim80\%$ computed at Rank 1, and an average joint verification accuracy of $96\%$ at a false match rate of 5\%, averaged across the three biometric modalities.
\item The representation is \textit{robust} across three modalities (iris, face and periocular), and different sensors (near-infrared iris sensors and visible smartphone sensors).
\item The joint embedding outperformed baselines that used state-of-the-art commercial biometric matchers and sensor identification schemes across three datasets corresponding to three biometric modalities and multi-spectral sensors.
\end{enumerate}

\section{Summary and Future Work}
\label{Sec:Sum}
In this paper, we proposed a one-shot method to simultaneously authenticate the user and the device from a single image, say a face or an iris image. To accomplish this task, we developed a method to learn a joint representation that can be used for combined biometric and sensor (device) recognition. The joint representation will be useful in remote application scenarios that employ multiple factor authentication. Examples include mobile banking on smartphones and accessing secure services. Additionally, the joint representation will be implicitly privacy-preserving as the biometric and sensor representations cannot be trivially separated. We used an embedding network to generate the joint representation. We evaluated the proposed approach by (i) exploring different training schemes, and (ii) testing the method on multiple datasets belonging to three different biometric modalities (iris, face and periocular) in both the identification and verification scenarios. We observed best performing results of identification accuracy of 99.81\% at Rank1 and a verification accuracy of TMR=100\% at 1\% FMR using the proposed method.

Future work will involve improving the current framework by incorporating domain knowledge (sensor characteristics) to further increase the recognition performances. We will also evaluate the proposed method on datasets with larger number of subjects and sensors.

\balance
\bibliographystyle{ieeetran}

redpajama_set_name: RedPajamaArXiv


text:
October 16, 2020 All about "Pork and wild boar"
October 16, 2020 All about "Dry peas / Split peas"
October 16, 2020 All about "Chickpea"
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All about "Cod", a fish rich in omega 3
The cod is amazing, prepared in brandade with which we will stuff a pepper or a zucchini. It is healthy and good simply poached and accompanied by some vegetables and an aioli to give it a little vigor. Unless you prefer your eggs, the Greeks prepare taramosalata.
Characteristics of cod:
Rich in proteins that contain the nine essential amino acids;
Source of omega 3;
Source of iodine and selenium;
Rich in vitamin B12;
Limits the risks of cardiovascular disease.
Definition of food
Cod identity card
Type: Fish;
Family: Gadidae;
Origin: Norway;
Season: January to April.
Characteristics of cod
Cod is a large fish 60 to 70 cm long and can weigh between 2 and 3 kg. It is generally gray or green in color.
Differences with nearby foods
The terms cod and cod are often confused. In the world of fishing, cod corresponds to adult cod while the word cod corresponds to young fish. In the kitchen, cod is called fresh, unprocessed cooked fish and cod is dried and salted fish fillets.
Word from the nutritionist
Cod is a semi-fatty fish that is rich in omega-3. It can be consumed once or twice a week. One serving corresponds to 150g of fish.
Per 100g of raw cod:
Nutrients Quantities
Protein 17.81 g
Carbohydrates 0 g
Water 81.22 g
Fibers 0 g
Vitamin B3 2.06 mg
Vitamin B12 0.91 µg
Vitamin D 0.54 µg
Selenium 33.1 µg
Phosphorus 203 mg
11 benefits of cod: why eat it?
Cod is a fish with little fat and is therefore considered a lean fish. In general, regular consumption of fish can provide several health benefits. A round table of international scientists concluded that the consumption of one to two fish meals per week was associated with the decrease in mortality from coronary heart disease.
Cod contains eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), two fatty acids from the omega-3 family. These fatty acids act as precursors of chemical messengers promoting the proper functioning of the immune, circulatory and hormonal systems. Several epidemiological and clinical studies have shown that the consumption of omega-3 fatty acids (mainly from fatty fish) exerts favorable effects on cardiovascular health and reduces mortality from cardiovascular disease. These fatty acids are known to act in several ways in the body. They act in particular by reducing blood pressure, blood triglycerides and the formation of blood clots, thereby reducing the risk of atherosclerosis. Furthermore, studies have shown that omega-3 fatty acids may play a role in regulating mood and preventing depression. Finally, these fatty acids would exert certain anti-inflammatory effects, which could be useful in the treatment of certain pathologies such as asthma, rheumatoid arthritis, inflammatory bowel disease and psoriasis.
Generally speaking, fish is an excellent source of complete protein since it contains the nine essential amino acids, i.e. acids that are not produced by our body and must come from our diet. Proteins are used primarily to form, repair and maintain tissues, such as the skin, muscles and bones. They are also used for the formation of digestive enzymes as well as hormones. In addition, several animal studies have shown that cod protein improves insulin sensitivity and increases the body's absorption of glucose.
Cod is an excellent source of selenium. This mineral works with one of the main antioxidant enzymes, thus preventing the formation of free radicals in the body. It also helps to convert thyroid hormones to their active form.
Atlantic cod from the St. Lawrence is an excellent source of iodine. It contains 15 times more than mackerel and 50 times more than rainbow trout (also from the St. Lawrence). Iodine is used in the composition of thyroid hormones necessary for the regulation of growth, development and metabolism.
Cod is an excellent source of vitamin B12. This vitamin works together with folic acid (vitamin B9) to make red blood cells in the blood. It also ensures the maintenance of nerve cells and cells that make bone tissue.
Cod is a good source of phosphorus. Phosphorus is the second most abundant mineral in the body after calcium. It plays an essential role in the formation and maintenance of healthy bones and teeth. In addition, it participates among other things in the growth and regeneration of tissues and helps to maintain normal blood pH. It is one of the constituents of cell membranes.
Cod is a good source of vitamin B3. Also called niacin, it participates in many metabolic reactions and contributes particularly to the production of energy from the carbohydrates, lipids, proteins and alcohol that we ingest. It also participates in the DNA formation process, allowing normal growth and development.
Cod is a good source of vitamin B6. Vitamin B6, also called pyridoxine, performs several vital functions in the body. It firstly participates in the production of red blood cells and allows them to transport more oxygen. Second, vitamin B6 helps regulate blood sugar levels. Finally, it contributes to the proper functioning of the immune system and plays a role in the metabolism (production and use) of proteins. This vitamin participates in energy metabolism and tissue formation.
Cod is a source of magnesium. Magnesium participates in bone development, protein construction, enzymatic actions, muscle contraction, dental health and the functioning of the immune system. It also plays a role in energy metabolism and in the transmission of nerve impulses.
Cod is a source of vitamin D. Vitamin D is closely involved in the health of bones and teeth, making calcium and phosphorus available in the blood, among other things for the growth of bone structure. Vitamin D also plays a role in the maturation of cells, including cells of the immune system.
Choosing the right cod
Atlantic cod is available year-round, fresh or frozen, usually in the form of skin-on fillets. In some fishmongers, you can also find Pacific cod, which is prepared in the same way, as well as sablefish, whose fatty flesh is prepared rather like that of salmon.
The different varieties
The main commercial species is Atlantic cod (Gadus morhua), followed by Pacific cod (Gadus macrocephalus). Also found in commerce is sablefish (Anoplopoma fimbria) which, despite its name, is not a real cod and whose flesh is more fatty.
In the refrigerator: As with all fresh fish, it is best to consume the cod on the day of purchase. At most, it can be stored for two days in the coldest part of the refrigerator.
In the freezer: A few months.
Cod preparation
How to cook it? How to match it?
In European cookbooks, salted cod is very often used, which is desalted for 24 hours in water before preparing it. In the absence of salted cod, fresh fish can be used, omitting the desalting stage.
The most common way to prepare cod is to poach it for 10 to 20 minutes in water or in a broth, drain it, remove the skin and bones, then crumble the flesh.
Omelet: brown the crumbled flesh in the oil and add it to beaten eggs, dill, salt and pepper. Cook in a pan. Or, for a more substantial omelet, first brown the potatoes cut into thin slices in the oil, add the cod flesh, cook until tender, add the beaten eggs and cook.
Cod with valentine: alternate layers of poached and flaked cod, rice and tomato sauce in a gratin dish, add a drizzle of melted butter, sprinkle with breadcrumbs and bake.
Warm salad: mix boiled and sliced potatoes, crumbled cod meat in a salad bowl, and incorporate a homemade vinaigrette with mustard; season with salt and pepper, garnish with chopped chives and serve this still warm salad.
Cold salad: beat poached cod, crumbled and cooled with homemade mayonnaise and chopped herbs. Serve on a bed of lettuce with a few slices of tomatoes.
In Provence, poached cod is accompanied by potatoes, carrots, green beans, cauliflower and boiled artichoke bottoms, as well as an aioli that is served separately. Hard-boiled eggs and snails usually complete this dish.
Brandade: pass the crumbled flesh through a blender or food processor. Put it back on the heat, adding little by little and alternately milk and oil without boiling. When a preparation with the consistency of a puree is obtained, salt, pepper and season with nutmeg. Serve with croutons rubbed with garlic and black olives. You can incorporate a boiled and mashed potato during cooking. You can stuff large macaroni, tomatoes, peppers, zucchini, etc. with this preparation.
Cod sparse: this African dish is usually prepared with cassava leaves, but you can replace them with chard or spinach leaves. Cook the leaves in boiling water, drain them by pressing them, then chop them. Add crushed tomato and finely chopped onion. Mash the poached and crumbled cod with a small pepper and place in a gratin dish. Cover with spinach leaves or chard, drizzle with oil and bake for 15 minutes in an oven set to 210 ° C (410 ° F).
Fierce avocado and cod: this West Indian dish is made with pan-fried cod and then cooled. Mash the crumbled flesh with avocado and a little lemon juice. Add a finely chopped hot pepper and cool for an hour in the refrigerator. Serve with toasted bread croutons.
Almond cod: cook fillets in a pan and garnish with slivered and golden almonds in butter.
Cod and cauliflower puree: mash with boiled cauliflower and poached and crumbled cod with a fork, adding a little milk or cream. Salt, pepper and reheat the preparation in a double boiler. Garnish with parsley.
With béchamel sauce: arrange the poached and crumbled cod in a gratin dish, coat with a béchamel sauce enriched with good cheese and put it in the oven for ten minutes.
Cod rougail: sauté minced onions and poached and crumbled cod in a little oil, stirring vigorously. Add minced garlic, thyme and coarsely chopped tomatoes. After a few minutes, add a glass of water. If desired, add chopped, seeded hot peppers. Cook, stirring, until all the liquid is absorbed. Serve over rice.
In chowder or bouillabaisse type soups.
Taramosalata: this culinary preparation based on cod eggs (or another fish) and olive oil, or crème fraîche, is often mixed with mashed potatoes or breadcrumbs. It is served as a starter.
The cod is also canned, air-dried (stockfish) or salted (kipper).
Allergy to proteins found there
Fish are among the nine most common food allergens. People allergic to fish react abnormally to the proteins found there. It is possible that a person with an allergy to a fish or a seafood is also allergic to other species belonging to the same group. For example, a person allergic to cod could also be allergic to salmon since both are fish. On the other hand, a person allergic to one type of fish or seafood (crustacean or fish or mollusk) will not necessarily be allergic to the other types. It is strongly recommended that people allergic to these foods consult an allergist before introducing new ones into their diet.
Fish is the main source of mercury to which we are exposed. This metal is naturally present in the environment, but the discharges caused by industrialization make the consumption of certain fish less safe for health. Predatory fish have high levels of mercury. Other fish species, such as cod, are said to have mercury levels below the norm. They can therefore be consumed without danger to health.
Cod history
It was first the term "moluel", which appeared in the 11th century, which served to designate this fish, the word "cod" having appeared later, in 1260. Its origin is obscure. It may come from the Celtic mor, "mer", and from the old French luz, "brochet" or "merlu", while others think that it comes rather from the old Provençal morrude, which means " gurnard ", another type of fish.
The term "cod" appeared in the language in 1278. It comes from the Dutch kabeljau. It is official in France and designates cod sold fresh or frozen.
Fish living in the cold waters of the globe, cod was a staple food among the peoples of the North, especially the Vikings who, during their warlike expansion between the 9th and 11th centuries, helped to spread its consumption throughout the rest of the Europe. Following them, the Basques and the Bretons, great navigators, will cross the Atlantic Ocean in search of this fish which, thanks to its lean flesh, can, once salted, keep for several months on long-distance boats. During the Middle Ages, they made a very lucrative business of it, the Catholics making it one of their main sources of protein during the 160 or so days of the year when they had to abstain from meat and dairy products.
The Sea Indians
Nearly 2,500 years before the "discovery" of America by Europeans, the Micmacs, and Algonquians, relied on the resources of the sea, and particularly on cod, to provide them with the greater part. of their food, hence the name "Sea Indians" given to them by the new arrivals.
The Basques and Bretons will push their boats to Stocafixa (Basque word meaning "Cod Island" and designating Newfoundland) and to Labrador where they will discover the famous Grand Banks, which are believed to have been the largest cod stocks in the world. Aware of the importance of their discovery, they will keep the location of their catches secret until a French explorer reveals it to the world. From then on, Portuguese, Spanish, French and English will rush to the Grand Banks to harvest this windfall.
This is how cod will spark the first colonization movements on the east coast of North America: villages and cities, whose economy is entirely based on it, are born where there were previously only rocks. and sand dunes. For almost 500 years, cod will shape the face of Canada's east coast. But the growing demand for this fish will lead to overfishing and, in the early 1990s, we witness a real collapse of stocks. Today, despite the imposition of moratoriums on the commercial fishery, Canadian stocks remain low and this fish, which for centuries has fed millions of poor families on both sides of the Atlantic, is now considered a luxury product.
For further
Although environmental factors may have played a role in the depletion of Atlantic cod stocks, many believe that this is mainly due to the trawl, a fishing gear that is harmful to the habitat of many marine and with very high bycatch. This is not the case with line fishing, which is practiced in parallel. In the United States as in Canada, anglers have joined associations to denounce the perverse effects of trawl fishing on the environment, on fish stocks and on the survival of riverside communities which have always practiced responsible fishing.
When the Atlantic cod stocks collapsed in the early 1990s, these fishermen were subject to the same restrictions as trawlers regardless of whether their methods were sustainable. The Cape Cod Commercial Hook Fishermen's Association, in collaboration with a foundation and a group of chefs from the Boston area, have therefore launched a campaign promoting cod caught by angling. Twenty restaurants in the region have committed to serving only fish from this source. This campaign, which sparked strong reactions from trawl fishermen, has been approved by the Monterey Bay Aquarium, which recommends that consumers eat Atlantic cod only if it is marked "caught by hand". line ".
Atlantic cod is not considered an eco-responsible choice because, despite severe management in the United States and Canada, the populations of this fish are overfished. Populations in Canadian areas are so low that some are categorized as endangered or threatened. Some environmental organizations like GreenPeace, Seachoice or OceanWise have open red and yellow lists to guide consumers in making more sustainable choices. Unfortunately, cod is on the red list.
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redpajama_set_name: RedPajamaCommonCrawl


When facing threats of violence and terrorism, law enforcement officers are often critical first responders. The ability of these officers to be alert, physically ready, and mentally prepared to handle the hazardous situations that are a regular part of the profession is essential to their agencies and the communities they protect. Fit for Duty, Third Edition With Online Video, provides practical information on creating and implementing physical fitness and wellness programs to help law enforcement officers fulfill their demanding job requirements.
Fit for Duty, Third Edition, is divided into four progressive sections. The text starts with big-picture information on fitness assessment, beginning with the general fitness levels of the entire nation and then focusing on how fit law enforcement officers compare to the general population. Part II explains the importance of physical fitness and how to train in each of those specific areas to increase cardiorespiratory endurance, muscular strength and endurance, explosive strength, flexibility, agility, speed, and anaerobic power. Part III focuses on lifestyle components of fitness, including diet and nutrition, weight management, stress management, smoking cessation, and the prevention of substance abuse. Part IV ties together all information from the previous sections into achievable plans and goals. It also explains how to avoid common hurdles and pitfalls of adopting lifestyle changes so that officers will have positive results. Throughout the text, exercise drills are featured in a numbered, step-by-step format so that people of all fitness levels can easily follow them.
With this text, law enforcement instructors and administrators can establish complete and customized fitness programs that prepare current and future officers in every branch of service. Individual officers will receive the tools they need to improve their fitness levels, which will help them in many situations they might encounter.
Chapter 1. What Does Fitness Mean to an Officer's Physical Readiness?
Chapter 2. What Does Fitness Mean to an Officer's Health and Well-Being?
Chapter 3. How Do You Start Being Active?
Chapter 4. How Fit Are You?
What Are Muscular Strength and Muscular Endurance?
What's So Bad About Cigarettes?
Robert Hoffman, MS, retired from the U.S. Army as a lieutenant colonel in 1991. During his 22 years in the military, Hoffman completed assignments around the world. He commanded a brigade headquarters company in Germany, a ranger company in Vietnam, and a Special Forces SCUBA detachment at Fort Bragg, North Carolina. He also commanded the 4th Ranger Training Battalion at Fort Benning, Georgia, where in addition to working with rangers, Hoffman trained U.S. drug enforcement agents who were being deployed in South America.
Hoffman spent three years as the director of training for the Army's Soldier Physical Fitness School and helped to develop the Army's Total Fitness program. He also spent four years as a professor in the department of physical education at West Point. While there, he was an assistant cross country and track coach and a junior varsity basketball coach.
Hoffman was certified as a fitness instructor by the American College of Sports Medicine (ACSM) and as a master fitness trainer by the U.S. Army. He received a master's degree in physical education from Indiana University and was a member of the American Society of Law Enforcement Trainers. Hoffman was also the author of Running Together: The Family Book of Jogging, and he helped write the army's Physical Fitness Training field manual.
Hoffman passed away in July 2016.
Thomas R. Collingwood, PhD, has been involved in implementing law enforcement programs for 40 years. He developed and directed the continuing education division of the Cooper Institute for Aerobics Research, where he created the institute's police instructor course that has trained more than 10,000 police fitness coordinators. He also designed the FitForce national law enforcement fitness program. Collingwood has worked with more than 200 law enforcement agencies worldwide to design fitness programs and has conducted validation studies to define job-related fitness standards for 100 federal, state, and local law enforcement agencies. He is the author of 10 books and more than 100 publications in the field.
Collingwood was a military policeman with the U.S. Army, a police psychologist with the Dallas Police Department, and a training director for the Kentucky Department of Justice. He has served as the national fitness director for the American Society of Law Enforcement Trainers and as a special advisor on law enforcement fitness to the President's Council on Physical Fitness and Sports and the International Association of Chiefs of Police (IACP). He also was an advisor for the redesign of the U.S. Army's Physical Readiness program.
Collingwood holds a master's degree in exercise science from the University of Kentucky and a doctorate in psychology from the University of Buffalo, and he is a certified health and fitness director with the American College of Sports Medicine. The IACP, U.S. Drug Enforcement Administration, U.S. Marshals Service, and U.S. Secret Service have all recognized Collingwood for his work in the field of law enforcement fitness. He was the recipient of the Healthy American Fitness Leaders award presented by the President's Council on Physical Fitness and Sports and the National Jaycees.
Collingwood resides with his wife, Gretchen, in Richardson, Texas.
Ancillary materials for this text are free to course adopters and available at www.HumanKinetics.com/FitForDuty.
Image bank. Includes all of the exercise technique photos and illustrations for easy-to-follow instruction. In addition, the image bank includes tables and copies of all checklists and forms from the book for easy distribution during classes and seminars.
Online video. Includes 40 video clips that demonstrate various test protocols and exercises found in the book. The videos explain and demonstrate the proper techniques for these tests and exercises so that users gain the most benefit from the workouts.


Over the past few years, I've felt the need for us as a society to step up to bigger challenges (e.g. waste, energy usage, access to education, etc.). Having studied engineering myself, at first, I thought that the answer lay purely in improving our technical problem solving skills. However, the more I've learned, the more I'm convinced that solutions in the absence of self-aware leadership and deep humanity are destined to fall flat. With that in mind, I've tried to figure out how to bridge the technical knowledge of engineering with the human capacity needed to create positive change, particularly, not simply using one as an add-on to the other, but looking for ways in which the two amplify each other. This exploration has led me to direct, manage, and facilitate the ILead (Institute for Leadership Education in Engineering) social innovation program, The Game.


text:
Hieróglifo ou hieroglifo (grego antigo, hieros, "sagrado" e glyphein, "gravar", "escrita sagrada") é como foram chamados cada um dos caracteres usados como escrita no Egito Antigo. As escritas logográficas que são pictográficas na forma de modo a lembrar o antigo egípcio, são às vezes também chamadas de "hieróglifos".

Lista de escritas e sistemas parecidos às vezes chamados de "hieroglíficos"
 Hieróglifos da Anatólia
 Hieróglifos astecas
 Hieróglifos chukoto
 Hieróglifos cretenses
 Hieróglifos egípcios
 Hieróglifos maias
 Hieroglifos de Mi'kmaq
 Hieróglifos de Muisca
 Hieróglifos de Ojibwe
 Hieróglifos olmecas
 Caracteres chineses

Ver também
 Arqueologia bíblica
 Jean-François Champollion
 Numerais egípcios
 Pedra de Roseta
 Pictograma

Outras leituras
 ALLEN, J. P. Middle Egyptian: an Introduction to the Language and Culture of Hieroglyphs. Cambridge: Cambridge University Press, 2001.
 FONTOURA JR., Antonio. Hieróglifos Egípcios: um Curso de Introdução à Leitura e Decifração de Textos do Antigo Egito. Curitiba: PatolaLivros, 2010.
 GARDINER, A. Egyptian Grammar: Being an Introduction to the Study of Hieroglyphs. Oxford: Griffith Institute, 1988.
 OLIVEIRA, Francis Lousada Rubiini de. A Escrita Sagrada do Egito Antigo. Dicionário Hieróglifo-Português. Ed. autor, 2008.
 PARKINSON, Richard. O Guia dos Hieróglifos Egípcios.  Editora Madras.

Ligações externas
 Hieróglifos Egípcios
 Princípios da leitura de hieróglifos egípcios

Egiptologia
Criptografia

redpajama_set_name: RedPajamaWikipedia


text:
import { parse } from 'src/util'
import * as actions from './actions'
import storage from '../../storage'

const REGEX_RE = /^\/(.*?)\/(\w*)/
const ANY_RE = new RegExp('.*', 'i')
const ENABLED_KEY = 'VUEX_ENABLED'
const enabled = storage.get(ENABLED_KEY)

const state = {
  enabled: enabled == null ? true : enabled,
  hasVuex: false,
  initial: null,
  base: null, // type Snapshot = { state: {}, getters: {} }
  inspectedIndex: -1,
  activeIndex: -1,
  history: [/* { mutation, timestamp, snapshot } */],
  initialCommit: Date.now(),
  lastCommit: Date.now(),
  filter: '',
  filterRegex: ANY_RE,
  filterRegexInvalid: false
}

const mutations = {
  'INIT' (state, snapshot) {
    state.initial = state.base = snapshot
    state.hasVuex = true
    reset(state)
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function reset (state) {
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function escapeStringForRegExp (str) {
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const getters = {
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export default {
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}

redpajama_set_name: RedPajamaGithub


text:
Penshurst, located at the foot of Mt Rouse, is steeped in volcanic history, showcased at the Volcanoes Discovery Centre. The dormant volcano Mt Rouse rises 100 metres above the surrounding plans at Penshurst, 20 minutes east of Hamilton.
The road access to the Mt Rouse Lookout provides panoramic views of the lava plain. A visit to the Volcanoes Discovery Centre will reveal the unique story of the formation of the lava flows known as the Kanawinka Geotrail.
Local Tips:
Enjoy the Penshurst Wetland Gardens, historic town walks, swimming at the local pool, sweeping views from Mt Rouse and find a delightful picnic spot to stop and relax.
Keep your eyes peeled for Penshurst's hidden gems, including wine bars, charming antique book stores and the native birdlife at Yatmerone Wildlife Reserve.
Penshurst was home to painter, mosaic, and stained glass artist Mervyn Napier Waller, who went on to create the wondrous Hall of Memory at the Australia War Memorial in Canberra.
Penshurst Store
Penshurst Store is a small country town bakery in Penshurst Victoria. Be welcomed by the friendly owners Amanda and baker Cam.Selling Rox Coffee,…
The Grey House Penshurst
Built over one hundred years ago, The Grey House has been sympathetically renovated with all modern amenities. The home has been designed to be…
McKenzies Pure Honey
McKenzies Pure Honey is based in Penshurst in South West Victoria, owned by local Apiarists Rene and Fraser McKenzie. Services include beekeeping…
Mount Rouse
Twenty minutes east of Hamilton lies the town of Penshurst, which is home to the extinct volcano Mount Rouse. The mountain is a massive accumulation…
Kanawinka Geotrail
Victoria's south-western volcanic plains are part of Australia's most extensive volcanic province. The Kanawinka Geotrail takes you on a journey…
Where to next? Discover more... The Grampians Way!


text:
General Tax Information »
Tax Deductions for Clothing
By: Tom Gresham
Donated used clothes offer tax benefits.
green bin used for recycling clothes, notice with instruction, image by hazel proudlove from Fotolia.com
1. Are Treadmills a Tax Write-Off?
2. Can Material Items Donated to a Church Be Tax Deductible?
3. Tax Deductions and Church Pledges
Donated used clothing is a common tax deduction, and the Internal Revenue Service has guidelines for what constitutes a tax-deductible donation and the proper way to register those donations on a tax return. In some cases, taxpayers can also claim deductions on certain clothes that they purchase to wear at work, but the tax code does not allow widespread use of this deduction.
Qualifying Organizations
Donated items, including clothes, qualify as tax-deductible only if they are given to "qualifying" organizations. The IRS grants tax-exemption status to certain groups, such as charitable and religious organizations and other nonprofits. Certain other entities also can receive tax-deductible contributions, such as government-operated units that use donations for public purposes. The IRS recommends that taxpayers ask to see an organization's letter from the IRS confirming its tax-exempt status or visit the IRS's online tool, EO Select Check, which lists exempt organizations.
Taxpayers can claim a deduction on donated clothes as long as the items are in good condition or better. However, an exception exists if taxpayers are claiming a deduction of more than $500 for an item of clothing. The exception might arise if a taxpayer has an expensive gown or pair of shoes that retain considerable value even in less-than-good condition -- for example, a gown that was worn in a famous movie scene. A taxpayer claiming a deduction of more than $500 on donated clothes, however, must have a qualified appraisal to back the claim.
Taxpayers receive a tax deduction for donated items equal to the estimated fair market value of the items. The IRS places the burden on the taxpayer to determine the fair market value of donated clothes and provides little guidance, except to note that the fair market value of used clothes tends to be significantly less than the clothing's price when bought new. The IRS recommends using the prices in consignment and thrift shops to determine fair prices for clothes. On a federal tax return, deductions for clothing and other non-cash contributions should be included on line 17 of Schedule A.
The IRS does not allow taxpayers to claim tax deductions for most clothes that they purchase for their own use at work, though there are exceptions. In particular, the clothes must be required as a condition of employment and they must not be suitable for everyday wear. For example, a particular fast-food uniform that is required, and which you could not wear elsewhere because it's covered with the restaurant's logos, would qualify. Safety gear, such as safety glasses or hard hats, also qualifies for a deduction. The definition excludes most types of required work clothes, however, such as business suits or painter overalls.
Internal Revenue Service: Charitable Contributions
Forbes: Claiming Tax Deductions for Clothing?
Internal Revenue Service: Organizations Eligible to Receive Tax-Deductible Charitable Contributions
Internal Revenue Service: Exempt Organizations Select Check
Tom Gresham is a freelance writer and public relations specialist who has been writing professionally since 1999. His articles have appeared in "The Washington Post," "Virginia Magazine," "Vermont Magazine," "Adirondack Life" and the "Southern Arts Journal," among other publications. He graduated from the University of Virginia.
Tax Deduction for Donating Glasses to Charity
IRS Tax Laws for Boat Tax Deductions and Donations
Tax Benefits From the Donation of Glasses
Tax Guidelines for Donated Furniture
Tax Deduction for Mileage Due to Voluntary Work
Tax Returns When Donating Money
How to List Goodwill Donations for Tax Deductions
Tax Deductions for Donating to a University
How to Get Furniture Appraised for Tax Write-Offs


EPPING — As the mosquito-related disease, Eastern Equine Encephalitis (EEE) plagues area communities, the School District is taking its own precautions.
Athletic Director Casey Todd presented the Epping School Board with an update on coaching positions, prevention for EEE on the field, and other athletic policies at Thursday night's meeting.
Todd presented plans for the new coaches' handbooks and the plan for reducing the threat of EEE and West Nile to students while they are out on the athletic fields.
All coaches will be given insect repellent spray and have been instructed to encourage the kids to use it before and during athletic training and events. Parents will have to sign a permission form for them to use school supplied repellent with DEET and students will also be allowed to bring their own repellent from home.
The School Board is also looking into the possibility of paying for spraying at the schools, because no town spraying is occurring this year.
The athletic department will again be using Access Sports Enhancement Training this year. Trainers will be available at all games and are accessible to all students with injuries. It was determined that athletes with injuries will need to check in with the Access trainer on Thursdays afternoons at 2:30 before participating in sports for the upcoming week.
Todd discussed the qualifications of the two new coaches, both formally of Portsmouth schools, that will be joining the Epping Athletic Department — Tim Hooley as girl's basketball coach and Tim Sullivan as the varsity boys basketball coach.
"I expect some good things from them," said Todd, "They both have excellent experience."
Coaches will be training to go over the districts new drug and alcohol policy with the students.
"We have strongly encouraged them to go over the new policies with the students very thoroughly," said Todd. "The students will also need to sign off on them."
The School Board also discussed the progress of the new building. The contractors are currently working on the phone lines and completing final corrections on the punch list, but report no major issues and expect no delays with opening the school on time.
It was decided that a community dedication and tours of the new building will be held and it was recommended that a committee be formed to organize it.
Both school meals and containers of milk throughout the Epping School District will be increasing in cost by 25 cents this year.
For additional information please contact Epping Superintendent of Schools office at 679-8003.


They say that "life is a cabaret, old chum," so why not enjoy a Cabaret cocktail?
This classic gin-based cocktail is an excellent accompaniment to a night at the theatre (or cabaret). It makes for a well-balanced, crisp drink best for after a meal or to accompany light snacks.
The cocktail dates back at least as far at the early 1960s, appearing in my trusty Old Mr. Boston De Luxe Official Bartender's Guide. It's a variation of sorts of the classic martini, but adds Benedictine and bitters. There are versions of this recipe that add absinthe and cherry brandy, but these are not part of the original recipe.
In a shaker over cracked ice, add 1 1/2 ounce of gin, 1/2 ounce of dry vermouth, and 1/4 ounce of benedictine. Shake and then strain into a three ounce cocktail glass. Garnish with a maraschino cherry.
How I like it: I stuck to the original proportions for this cocktail instead of scaling it for today's larger portions. It's best served this way. It's not a grandiose drink. Also, though I used Bombay Sapphire for the gin, I recommend the original Bombay Dry Gin for a crisper flavor.
I should note that when I first whipped up this cocktail, I accidentally grabbed a bottle of Drambuie, adding it to the drink in place of the Benedictine. This happy mistake actually makes for a pleasant, sweeter cocktail. Something to consider serving as an alternative to those who aren't fans of dry martinis.
Come to the cabaret, but drink responsibly!


I planned to use the tree photo to teach farmers and schoolchildren to care for the land through planting and caring for trees. Luckily for me, the chief of police was raised a Catholic, and was willing to listen as I explained that all people, especially Christians who were raised to know that God created the heavens and earth, must understand that all God's creation is good (Gen.1). Many Maryknoll missioners like me see the impact of environmental injustices and climate change on the poor and less fortunate people we serve.
Everyone, not just priests, should be concerned about the environment. Through the works of the Creator we can begin to know the Creator." - Fr. Lawrence Radice, M.M.


text:
Airfares to Auckland
From Perth
Flights from Perth to Auckland
Compare Cheap Flights from Perth to Auckland, New Zealand
Looking for cheap flights to Auckland from Perth? Use Webjet's deal finder to help you compare flights, carriers and seating classes all year round. You'll be sure to find the right flight to match your budget, plans and travel preferences.
Mon 29 Jul	 Best Price
Updated on 17 Jul
Auckland Guide
Cheapest time to fly to Auckland
Cheapest Price from Perth to Auckland
Currency used in Auckland
Average Time to Auckland
Current Time in Auckland
Average Temperature in Auckland
Average Rainfall in Auckland
See more of Auckland
Landmarks and Monuments You Can't Miss
Once arriving on flights Perth to Auckland, you can't miss the Sky Tower. It is one of the most prominent features of Auckland's cityscape and is easily pinpointed from almost anywhere around the city and beyond. Make for the top of the Sky Tower for remarkable views worhty of any holiday happysnap. For an extra level of adventure after your cheap flights to Auckland from Perth, you could even walk along the top of the tower or jump off the Sky Tower in a 192 metre free-fall.
To experience some nature after your cheap flights Perth to Auckland, take a day trip to Rangitoto Island. On the island, you can hike around one of the Auckland area's famous volcanoes. The island is also home to some nice beaches, views of the city, kayaking excursions, and plenty of native plants and animals.
There are several volcanoes to experience when you book Perth to Auckland flights, and one of them is right in the city. Mount Eden is a dormant volcano situated just south of Auckland's CBD. Visitors with plane tickets to Auckland from Perth can hike up Mount Eden for a unique view over the city with the rim of the volcano in the foreground.
New Zealand is world-famous for its wine. After booking Perth to Auckland return flights, Waiheke Island is the place to go for a range of vineyards in a scenic location. The island is only 40 minutes from Auckland by ferry and offers a quiet escape from the hustle and bustle of the city. In addition to vineyards, Waiheke Island has some great restaurants and plenty of water activities to enjoy.
Wintergardens
If you want to experience some green space after arriving into Auckland Airport (AKL), pay a visit to the Wintergardens within the Auckland Domain. The gardens consist of beautiful plants and trees as well as greenhouses which house tropical flowers. For a unique experience, plan your flights Perth to Auckland around the blooming of the gardens' Corpse Flower. It only blooms every seven to 10 years and brings in huge crowds despite its notoriously bad smell.
Auckland Activity Ideas For Those Travelling with Children
A popular activity for families with Perth to Auckland flights is the Auckland Zoo, which is located west of the city centre. The zoo features a range of different animals, including native fauna and animals from all over the world. Visit during the day to see animals such as elephants, cheetahs, penguins, giraffes, and kiwi birds. Safari nights are also available for visitors with flights Perth to Auckland in order to see what the animals get up to after the zoo closes at the end of the day.
Butterfly Creek is located right beside Auckland Airport (AKL) and is a popular destination for families with cheap flights to Auckland from Perth. A large butterfly house is the main attraction within the park. Children can also view and learn about a variety of animals, including crocodiles and native birds. There is also a Dinosaur Kingdom, which features 60 life-size and realistic dinosaur statues that kids love.
Waitakere Ranges Regional Park
The Waitakere Ranges are located in southwestern Auckland and make for a nice day out for families. Throughout the park, there are 250 kilometres (155 miles) of hiking and walking trails, which range in length and difficulty. Visitors with Perth to Auckland flights can enjoy the park's black-sand beaches, waterfalls, and rugged nature.
Public Transport and How to Get Around
You'll have no trouble making your way around the city after your cheap flights Perth to Auckland. The Auckland region features trains, buses, and ferries to help you explore. Train stops are available throughout the CBD and connect with neighbourhoods such as Mount Eden, Parnell, and Newmarket. There are lots of bus lines through Auckland, with the Link bus service offering an easy route for visitors with Perth to Auckland flights. The CityLink makes stops throughout the CBD, the InnerLink travels into suburbs such as Ponsonby, and the OuterLink journeys to Mount Eden as well as further out neighbourhoods. Since Auckland is located between two harbours, ferry travel is a popular mode of transport for visitors with flights Perth to Auckland. Ferries are available between the CBD and popular spots, including Devonport, Waiheke Island, and Rangitoto. Auckland also has plenty of taxis and Ubers to provide door-to-door service throughout the city and its suburbs.


RENI's solo song was delivered.
A new song of RENI who will perform a solo concert on March 9 this year began delivery on March 8th one day ago.
The songs that were released are two songs "Jirettai-na" (means 'tantalizing') and "spart!".


text:
Fatty Acid Uptake and Lipid Storage Induced by HIF-1 alpha Contribute to Cell Growth and Survival after Hypoxia-Reoxygenation
Please always quote using this URN: urn:nbn:de:bvb:20-opus-115162
Karim Bensaad, Elena Favaro, Caroline A. Lewis, Barrie Peck, Simon Lord, Jennifer M. Collins, Katherine E. Pinnick, Simon Wigfield, Francesca M. Buffa, Ji-Liang Li, Qifeng Zhang, Michael J. O. Wakelam, Fredrik Karpe, Almut Schulze, Adrian L. Harris
An in vivo model of antiangiogenic therapy allowed us to identify genes upregulated by bevacizumab treatment, including Fatty Acid Binding Protein 3 (FABP3) and FABP7, both of which are involved in fatty acid uptake. In vitro, both were induced by hypoxia in a hypoxia-inducible factor-1 alpha (HIF-1 alpha)-dependent manner. There was a significant lipid droplet (LD) accumulation in hypoxia that was time and O-2 concentration dependent. Knockdown of endogenous expression of FABP3, FABP7, or Adipophilin (an essential LD structural component)An in vivo model of antiangiogenic therapy allowed us to identify genes upregulated by bevacizumab treatment, including Fatty Acid Binding Protein 3 (FABP3) and FABP7, both of which are involved in fatty acid uptake. In vitro, both were induced by hypoxia in a hypoxia-inducible factor-1 alpha (HIF-1 alpha)-dependent manner. There was a significant lipid droplet (LD) accumulation in hypoxia that was time and O-2 concentration dependent. Knockdown of endogenous expression of FABP3, FABP7, or Adipophilin (an essential LD structural component) significantly impaired LD formation under hypoxia. We showed that LD accumulation is due to FABP3/7-dependent fatty acid uptake while de novo fatty acid synthesis is repressed in hypoxia. We also showed that ATP production occurs via beta-oxidation or glycogen degradation in a cell-type-dependent manner in hypoxia-reoxygenation. Finally, inhibition of lipid storage reduced protection against reactive oxygen species toxicity, decreased the survival of cells subjected to hypoxia-reoxygenation in vitro, and strongly impaired tumorigenesis in vivo.…
urn:nbn:de:bvb:20-opus-115162
Medizinische Fakultät / Theodor-Boveri-Institut für Biowissenschaften
Cell Reports
Cell Reports 9, 349–365. DOI: 10.1016/j.celrep.2014.08.056
https://doi.org/10.1016/j.celrep.2014.08.056
http://www.ncbi.nlm.nih.gov/pubmed?term=25263561
beta-oxidation; binding protein; cancer cell; complex-III; inducible factor-I; lipogenesis; metabolism; proliferation; resistance; triglyceride accumulation
CC BY-NC-ND: Creative-Commons-Lizenz: Namensnennung, Nicht kommerziell, Keine Bearbeitung


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