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: A Survey of Security and Privacy Issues for Biometrics Based Remote Authentication in Cloud 
Chapter Title:  Authentication Based on Singular Biometrics Features 
1. Abstract of Cited Paper (BIB001):  Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points. 
2. Abstract of Cited Paper (BIB002):  Automated security is one of the major concerns of modern times. Secure and reliable authentication systems are in great demand. A biometric trait like electrocardiogram (ECG) of a person is unique and secure. In this paper, we propose a human authentication system based on ECG waves considering a plotted ECG wave signal as an image. The Radon Transform is applied on the preprocessed ECG image to get a radon image consisting of projections for θ varying from 0 o to 180 o . The pairwise distance between the columns of Radon image is computed to get a feature vector. Correlation Coefficient between feature vector stored in the database and that of input image is computed to check the authenticity of a person. Then the confusion matrix is generated to find False Acceptance Ratio (FAR) and False Rejection Ratio (FRR). This methodology of authentication is tested on ECG wave data set of 105 individuals taken from Physionet QT Database. The proposed authentication system is found to have FAR of about 3.19% and FRR of about 0.128%. The overall accuracy of the system is found to be 99.85%. 
3. Abstract of Cited Paper (BIB003):  Preprocessing is an important step in the area of image processing and pattern recognition. This paper aims to present a review of recent as well as classic fingerprint image enhancement techniques. The umbrella of techniques used for evaluation varies from histogram based enhancement, frequency transformation based, Gabor filter based enhancement and its variants to composite enhancement technique. The effectiveness of enhancement techniques proposed by various researchers is evaluated on the basis of peak signal to noise ratio and equal error rate which refers to robustness and stability of identification process. Experimental results shows that incorporating the enhancement technique based on Gabor filter in wavelet domain and composite method improves equal error rate .Improved error rate and peak signal noise ratio improves the identification/verification accuracy marginally. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in enhancement of fingerprint images which is essential preprocessing step in automatic fingerprint identification and verification. 
4. Abstract of Cited Paper (BIB004):  The paper describes a fingerprint recognition system consisting of image preprocessing, filtration, feature extraction and matching for recognition. The image preprocessing includes normalization based on mean value and variation. The orientation field is extracted and Gabor filter is used to prepare the fingerprint image for further processing. For singular point detection the Poincare index with partitioning method is used. The ridgeline thinning is presented and so is the minutia extraction by CN algorithm. Different Toeplitz matrix descriptions are in the work. Their behavior against abstract set of points is tested. The presented algorithm has proved its successful use in identification of shifted, rotated and partial acquired fingerprints without additional scaling. 
5. Abstract of Cited Paper (BIB005):  This paper explores the effectiveness of a novel multibiometric system that is resulted from the fusion of the electrocardiogram (ECG) with an unobtrusive biometric face and another biometric fingerprint which is known to be a least obtrusive for efficient individual authentication. The unimodal systems of the face and the fingerprint biometrics are neither secure nor they can achieve the optimum performance. Using the ECG signal as one of the biometrics offer advantage to a multibiometric system that ECG is inherited to an individual which is confidential, secured and difficult to be forged. It has an inherent feature of vitality signs that ensures a strong protection against spoof attacks to the system. Transformation based score fusion technique is used to measure the performance of the fused system. In particular, the weighted sum of score rule is used where weights are computed using equal error rate (EER) and match score distributions of the unimodal systems. The performance of the proposed multibiometric system is measured using EER and receiver operating characteristic (ROC) curve. The results show the optimum performance of the multibiometric system fusing the ECG signal with the face and fingerprint biometrics which is achieved to an EER of 0.22%, as compared to the unimodal systems that have the EER of 10.80%, 4.52% and 2.12%, respectively for the ECG signal, face and fingerprint biometrics. 
6. Abstract of Cited Paper (BIB006):  In this paper we presented one fresh approach where the authentic user's typing credentials are combined with the password to make authentication convincingly more secure than the usual password used in both offline and online transactions. With the help of empirical data and prototype implementation of the approach, we justified that our approach is ease of use, improved in security and performance. In normal approach different keystroke event timing is used for user profile creation. Keystroke latency and duration is inadequate for user authentication, which motivates exploring other matrices. In this paper we proposed combination of different matrices and calculation of degree of disorder on keystroke latency as well as duration to generate user profile. Statistical analysis on these matrices evaluates enhanced authentication process. 
7. Abstract of Cited Paper (BIB007):  The Check Your Biosignals Here initiative (CYBHi) was developed as a way of creating a dataset and consistently repeatable acquisition framework, to further extend research in electrocardiographic (ECG) biometrics. In particular, our work targets the novel trend towards off-the-person data acquisition, which opens a broad new set of challenges and opportunities both for research and industry. While datasets with ECG signals collected using medical grade equipment at the chest can be easily found, for off-the-person ECG data the solution is generally for each team to collect their own corpus at considerable expense of resources. In this paper we describe the context, experimental considerations, methods, and preliminary findings of two public datasets created by our team, one for short-term and another for long-term assessment, with ECG data collected at the hand palms and fingers.

Literature Review:
Fingerprint based authentication BIB004 is the most popularly used technique in cloud environment because of its acceptability, uniqueness and immutability. Keystroke pattern analysis is another popular choice to authenticate users. Most important requirement of biometrics based remote authentication is liveliness detection, which is feasible by ECG based authentication. For this reason, a brief idea about some of the existing works on fingerprint, keystroke and ECG is presented here, where template data protection may not be major concern. During fingerprint verification, several image enhancement techniques BIB004 could be used such as histogram based, frequency transformation based, Gabor filter based enhancement. Gabor filter in wavelet domain improves accuracy level of fingerprint verification. Concept of Toeplitz matrix BIB005 is considered for fingerprint verification without information loss about fingerprint image. In BIB001 , fingerprint search algorithm was proposed based on database clustering. It speeds up the search process and improves the retrieval accuracy. Fingerprint can be verified by using spectral minutiae representation BIB002 which can be either location based (SML) or orientation based (SMO). Both techniques have pros and cons in different scenarios. However, fusion of two techniques shows better result. Fingerprint verifier enables template protection scheme along with fixed length feature vectors. Work on keystroke analysis is vast in the literature  . Most of the user authentication techniques are based on measuring the distance between the user keystroke pattern and enrolled keystroke for fixed text or free text. Assessment of keystroke dynamics is based on the traditional statistical analysis or relatively newer pattern recognition techniques such as z-test, Bayesian classifiers, and neural network. Pattern matching may suffer from long search times. This issue can be solved by clustering user profiles. In BIB007 , a combined standard deviation of keystroke duration and degree of disorder for keystroke latency along with disorder for keystroke duration is used for identification and authentication. In pressure-based user authentication system, the discrete time signal is transformed into the frequency domain. It is costly as it requires additional hardware to measure keystroke pressure. In a few cases, instability of typing patterns is reduced by introducing artificial rhythm concept. Keystroke recognition has been investigated for the virtual keyboard that used to interact with the hand held electronic devices, such as PDAs, and mobile phones. In ECG based authentication, correlation coefficient BIB003 between input image and feature vector stored in database is computed to check authentic person. Confusion matrix is generated to determine False Acceptance Rate (FAR) and False Rejection Rate (FRR). Gurkanet al.  proposed ECG authentication technique based on combining AC/DCT features, Mel-Frequency Cepstrum Coefficients (MFCC) features and QRS beat information of the Lead-I ECG signals. According to BIB008 , authentication is possible by collecting ECG signal from fingertips. However, it may contain more noise compared to data collected directly from heart. In BIB006 , feasibility of ECG is tested in a multi-biometric framework where it combines with fingerprint biometrics for individual authentication. Performance is evaluated using Equal Error Rate (EER), Receiver Operating Characteristics (ROC).