Machine Learning for Biometrics: Concepts, Algorithms and Applications

Front Matter
Copyright
Contributors
Preface
Acknowledgments
Machine learning approach for longitudinal face recognition of children
Introduction
Face modality for children face recognition
Children face databases
Newborn face database
Newborn, infants, and toddlers longitudinal face database (NITL)
Children multimodal biometric database (CMBD)
Extended newborn face database
Face recognition of children
Database preprocessing
COTS-Commercial off-the-shelf face matchers
Feature extraction techniques
PCA-Principal component analysis
LDA-Linear discriminant algorithm
Machine learning classification methods
Logistic regression
K-Nearest Neighbors
Decision Tree
Support Vector Machine
Gaussian Naive Bayes
Deep learning approach
Results
K-fold cross validation
Leave-one-out cross validation
Discussion and conclusion
References
Thermal biometric face recognition (TBFR): A noncontact face biometry
Introduction
Outline of aim and objective of problem
Chapter organization
Existing approaches
Proposed work
Thermal face detection
Face landmarking
Feature extraction
Facial geometrical biometry identification and feature fusion
Deep subspace clustering for biometric recognition
Final biometrically clustered faces
Experimental results
Choice of database
Results and discussion
Deep learning-based feature extractors compared
Methods compared
Conclusion
References
Multimodal biometric recognition using human ear and profile face: An improved approach
Introduction
Related work
Background
Steerable pyramid transform
Local texture descriptors
Proposed multimodal biometric recognition
Preprocessing
Feature extraction using block-based SPT-BSIF descriptor
Matching and classification
Multimodal fusion
Feature-level fusion
Score-level fusion
Experiments and results
Datasets
Experimental setup
Results
Comparison with state-of-the-art methods
Discussion
Conclusion
References
Statistical measures for Palmprint image enhancement
Introduction
Palmprint image enhancement
Spatial domain-based enhancement
Power law transform
Negative image
Log transform
Frequency domain-based enhancement
Gaussian low-pass filter (GLPF)
Gaussian high-pass filter (GHPF)
Butterworth low-pass filter (BLPF)
Butterworth high-pass filter (BHPF)
Image restoration
Median filter (MF)
Contrast limited adaptive histogram equalization (CLAHE)
Decorrelation stretch (DS)
2D adaptive filter
2D order statistic filter
Adjusted image
Statistical measure
Subjective image quality assessment
Objective image quality assessment
Discussion and analysis
Conclusion
References
Retina biometrics for personal authentication
Introduction
Framework of authentication system using biometrics
Related work
Face authentication
Iris authentication
Palmprint authentication
Fingerprint authentication
Ear
Voice
Signature
Keystroke dynamics
Gaits
Anatomy of retina
ANFIS-based retina biometric authentication system (ARBAS)
Training phase
Testing phase
Performance metrics for authentication system
Experimental setup
Conclusion
References
Gender recognition from facial images using multichannel deep learning framework
Introduction
Literature review
Preliminaries
Deep learning
Pretrained networks
AlexNet
GoogLeNet
Proposed system
Steps
Featured image formation
Local directional pattern (LDP) image formation
Edge direction magnitude computation
Highest edge direction magnitude identification
LDP image computation
Shallow CNN
Feature fusion
Canonical correlation analysis (CCA)
Discriminant correlation analysis (DCA)
Experimental results and discussion
Adience dataset
Single-channel approach
Proposed MC-DLF approach
Summary
References
Implementation of cardiac signal for biometric recognition from facial video
Introduction
Biometric identification system based on facial video
Face detection and video acquisition
Feature extraction
Identification
Algorithm for implementation
Results and discussions
Experimental environment
Performance evaluation
Comparison of physiological datasets for emotion recognition
Conclusions and future work
References
Real-time emotion engagement tracking of students using human biometric emotion intensities
Introduction
Related work
Methodology
Preprocessing steps for initial dataset preparation
Feature extraction technique
Interpreting FACS database
Step-wise algorithm
Training and real-time classification
Advantage and disadvantages of the approach
Results and discussions
Conclusions
References
Facial identification expression-based attendance monitoring and emotion detection-A deep CNN approach
Introduction
Related work
Proposed work
Facial recognition
Face acquisition, processing, and recognition
Evaluation
Emotion recognition
Image gathering and preprocessing
Haar cascade classifiers
VGG-16 network and modelling
Testing and results
Conclusion and future work
References
Contemporary survey on effectiveness of machine and deep learning techniques for cyber security
Introduction
Categories of cyber attacks
Malware
Phishing
Social engineering
Man-in-the-middle (MitM) attack
Zero-day attack
Biometrics in cyber security
Fingerprint scanning
Voice recognition
Voice-speaker identification
Voice-speaker verification
Facial recognition
Signature recognition
Iris scanner and recognition
Veins recognition
DNA biometrics system
Cyber attacks versus machine learning
Applications of ML and DL in cyber security
Regression
Classification
Clustering
Association rule learning
Dimensionality reduction
Generative models
Machine learning-based cyber security systems
Network protection system using ML
ML for endpoint protection
Machine learning for application security
ML for user behavior
ML for process behavior
Deep learning-based cyber security systems
Intrusion detection/intrusion prevention (ID/IP) systems
Malware detection
Social engineering and spam detection
Network traffic analysis
Experimental data
Web site defacement detection
Conclusion and future scope
References
A secure biometric authentication system for smart environment using reversible data hiding through encryptio ...
Introduction
Literature review
Proposed scheme
Illustrative examples
Experimental study and result analysis
Conclusion
References
An efficient and untraceable authentication protocol for cloud-based healthcare system
Introduction
Contribution
Related work
System model and security goals
Network model
Threat model
Security goals
Biometric hashing
Proposed protocol
Registration phase
Doctor registration
Patient registration
Patient data upload phase (PUP)
Treatment phase (TP)
Checkup phase (CP)
Security analysis
Formal security analysis using real-or-random model
Security model
Informal security analysis
Provides patient privacy
Resistance to health report forgery attack
Resistance to man-in-the-middle attack
Resistance to replay attack
Resistance to stolen mobile device attack
Provision of nonrepudiation
Simulation of the protocol
Performance analysis
Conclusion
References
Index
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