You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Detecting Faces in Images : A Survey 
Chapter Title:  Hidden Markov Model 
1. Abstract of Cited Paper (BIB001):  Abstract This paper describes an approach to the problem of face identification which uses Hidden Markov Models (HMM) to represent the statistics of facial images. HMMs have previously been used with considerable success in speech recognition. Here we describe how two-dimensional face images can be converted into one-dimensional sequences to allow similar techniques to be applied. We investigate the factors that affect the choice of model type and model parameters. We show how a HMM can be used to automatically segment face images and extract features that can be used for identification. Successful results are obtained when facial expression, face details and lighting vary. Small head orientation changes are also tolerated. Experiments are described which assess the performance of the HMM-based approach and the results are compared with the well-known Eigenface method. For the given test set of 50 images, the HMM approach performs favourably. We conclude by summarizing the benefits of using HMMs in this area, and indicate future directions of work. 
2. Abstract of Cited Paper (BIB002):  The work presented in this paper describes a hidden Markov model (HMM)-based framework for face recognition and face detection. The observation vectors used to characterize the states of the HMM are obtained using the coefficients of the Karhunen-Loeve transform (KLT). The face recognition method presented reduces significantly the computational complexity of previous HMM-based face recognition systems, while slightly improving the recognition rate. Consistent with the HMM model of the face, this paper introduces a novel HMM-based face detection approach using the same feature extraction techniques used for face recognition. 
3. Abstract of Cited Paper (BIB003):  Two new schemes are presented for finding human faces in a photograph. The first scheme approximates the unknown distributions of the face and the face-like manifolds wing higher order statistics (HOS). An HOS-based data clustering algorithm is also proposed. In the second scheme, the face to non-face and non-face to face transitions are learnt using a hidden Markov model (HMM). The HMM parameters are estimated corresponding to a given photograph and the faces are located by examining the optimal state sequence of the HMM. Experimental results are presented on the performance of both the schemes. 
4. Abstract of Cited Paper (BIB004):  We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face" and "nonface" model clusters. At each image location, a difference feature vector is computed between the local image pattern and the distribution-based model. A trained classifier determines, based on the difference feature vector measurements, whether or not a human face exists at the current image location. We show empirically that the distance metric we adopt for computing difference feature vectors, and the "nonface" clusters we include in our distribution-based model, are both critical for the success of our system. 
5. Abstract of Cited Paper (BIB005):  We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.

Literature Review:
The underlying assumption of the Hidden Markov Model (HMM) is that patterns can be characterized as a parametric random process and that the parameters of this process can be estimated in a precise, well-defined manner. In developing an HMM for a pattern recognition problem, a number of hidden states need to be decided first to form a model. Then, one can train HMM to learn the transitional probability between states from the examples where each example is represented as a sequence of observations. The goal of training an HMM is to maximize the probability of observing the training data by adjusting the parameters in an HMM model with the standard Viterbi segmentation method and Baum-Welch algorithms BIB001 . After the HMM has been trained, the output probability of an observation determines the class to which it belongs. Intuitively, a face pattern can be divided into several regions such as the forehead, eyes, nose, mouth, and chin. A face pattern can then be recognized by a process in which these regions are observed in an appropriate order (from top to bottom and left to right). Instead of relying on accurate alignment as in template matching or appearancebased methods (where facial features such as eyes and noses need to be aligned well with respect to a reference point), this approach aims to associate facial regions with the states of a continuous density Hidden Markov Model. HMM-based methods usually treat a face pattern as a sequence of observation vectors where each vector is a strip of pixels, as shown in Fig. 11a . During training and testing, an image is scanned in some order (usually from top to bottom) and an observation is taken as a block of pixels, as shown in Fig. 11a . For face patterns, the boundaries between strips of pixels are represented by probabilistic transitions between states, as shown in Fig. 11b , and the image data within a region is modeled by a multivariate Gaussian distribution. An observation sequence consists of all intensity values from each block. The output states correspond to the classes to which the observations belong. After the HMM has been trained, the output probability of an observation determines the class to which it belongs. HMMs have been applied to both face recognition and localization. Samaria  showed that the states of the HMM he trained corresponds to facial regions, as shown in Samaria and Young applied 1D and pseudo 2D HMMs to facial feature extraction and face recognition BIB002 ,  . Their HMMs exploit the structure of a face to enforce constraints on the state transitions. Since significant facial regions such as hair, forehead, eyes, nose, and mouth occur in the natural order from top to bottom, each of these regions is assigned to a state in a one-dimensional continuous HMM. Fig. 11b shows these five hidden states. For training, each image is uniformly segmented, from top to bottom into five states (i.e., each image is divided into five nonoverlapping regions of equal size). The uniform segmentation is then replaced by the Viterbi segmentation and the parameters in the HMM are reestimated using the Baum-Welch algorithm. As shown in Fig. 11a , each face image of width W and height H is divided into overlapping blocks of height L and width W . There are P rows of overlap between consecutive blocks in the vertical direction. These blocks form an observation sequence for the image, and the trained HMM is used to determine the output state. Similar to BIB002 , Nefian and Hayes applied HMMs and the Karhunen Loève Transform (KLT) to face localization and recognition BIB003 . Instead of using raw intensity values, the observation vectors consist of the (KLT) coefficients computed from the input vectors. Their experimental results on face recognition show a better recognition rate than BIB002 . On the MIT database, which contains 432 images each with a single face, this pseudo 2D HMM system has a success rate of 90 percent. Rajagopalan et al. proposed two probabilistic methods for face detection BIB004 . In contrast to BIB005 , which uses a set of multivariate Gaussians to model the distribution of face patterns, the first method in BIB004 uses higher order statistics (HOS) for density estimation. Similar to BIB005 , both the unknown distributions of faces and nonfaces are clustered using six density functions based on higher order statistics of the patterns. As in  , a multilayer perceptron is used for classification, and the input vector consists of twelve distance measures (i.e., log probability) between the image pattern and the twelve model clusters. The second method in BIB004 uses an HMM to learn the face to nonface and nonface to face transitions in an image. This approach is based on generating an observation sequence from the image and learning the HMM parameters corresponding to this sequence. The observation sequence to be learned is first generated by computing the distance of the subimage to the centers of the 12 face and nonface cluster centers estimated in the first method. After the learning completes, the optimal state sequence is further processed for binary classification. Experimental results show that both HOS and HMM methods have a higher detection rate than BIB006 , BIB005 , but with more false alarms.