Face detection and recognition are the nonintrusive biometrics of choice in many security applications. Examples of their use include border control, driver's license issuance, law enforcement investigations, and physical access control.
Face Detection and Recognition: Theory and Practice elaborates on and explains the theory and practice of face detection and recognition systems currently in vogue. The book begins with an introduction to the state of the art, offering a general review of the available methods and an indication of future research using cognitive neurophysiology. The text then:
Explores subspace methods for dimensionality reduction in face image processing, statistical methods applied to face detection, and intelligent face detection methods dominated by the use of artificial neural networks Covers face detection with colour and infrared face images, face detection in real time, face detection and recognition using set estimation theory, face recognition using evolutionary algorithms, and face recognition in frequency domain Discusses methods for the localization of face landmarks helpful in face recognition, methods of generating synthetic face images using set estimation theory, and databases of face images available for testing and training systems Features pictorial descriptions of every algorithm as well as downloadable source code (in MATLAB (R)/PYTHON) and hardware implementation strategies with code examples Demonstrates how frequency domain correlation techniques can be used supplying exhaustive test results Face Detection and Recognition: Theory and Practice provides students, researchers, and practitioners with a single source for cutting-edge information on the major approaches, algorithms, and technologies used in automated face detection and recognition.
Asit Kumar Datta (University of Calcutta Kolkata India)
, Madhura Datta (University of Calcutta
, Pradipta Kumar Banerjee (Future Institute of Engineering and Management
Apple Academic Press Inc.
Country of Publication:
Professional and scholarly
Introduction Introduction Biometric identity authentication techniques Face as biometric identity Automated face recognition system Process flow in face recognition system Problems of face identification and recognition techniques Liveness detection for face recognition Tests and metrics Cognitive psychology in face recognition Face detection and recognition techniques Introduction to face detection Feature based approaches for face detection Low level analysis Active shape model Feature analysis Image based approaches for face detection Statistical approaches Face recognition methods Geometric feature based method Subspace based face recognition Neural network based face recognition Correlation based method Matching pursuit based methods Support vector machine approach Selected works on face classifiers Face reconstruction techniques Three dimensional face recognition Subspace based face recognition Introduction Principal component analysis Two dimensional principal component analysis Kernel principal component analysis Fisher linear discriminant analysis Fisher linear discriminant analysis for two class case Independent component analysis Face detection by Bayesian approach Introduction Bayes decision rule for classification Gaussian distribution Bayes theorem Bayesian decision boundaries and discriminant function Density estimation using eigenspace decomposition Bayesian discriminant features (BDF) method for face detection Modelling of face and non-face pattern Bayes classification using BDF Experiments and results Face detection in colour and infrared images Introduction Face detection in colour images Colour spaces RGB model HSI colour model YCbCr colour space Face detection from skin regions Skin modelling Probabilistic skin detection Face detection by localizing facial features Eye map Mouth map Face detection in infrared images Multivariate histogram based image segmentation Method for finding major clusters from a multivariate histogram Experiments and results on the colour and IR face image datasets Utility of facial features Intelligent face detection Introduction Multilayer perceptron model Learning algorithm Face detection networks Training images Data preparation Face training Exhaustive training Evaluation of face detection for upright faces Algorithm Image scanning and face detection Real time face detection Introduction Features Integral image Rectangular feature calculation from integral image ADABOOST Modified ADABOOST algorithm Cascade classifier Face detection using OpenCV Face space boundary selection for face detection and recognition Introduction Face points, face classes and face space boundaries for face detection and recognition Mathematical preliminaries for set estimation method Face space boundary selection using set estimation for face detection Algorithm for global threshold based face detection Experimental design and result analysis Face / non-face classification using global threshold during face detection Comparison between threshold selections by ROC based and set estimation based techniques Classification of face / non-face regions of an image containing multiple faces Class specific thresholds of face-class boundaries for face recognition Experimental design and result analysis on face datasets for face recognition Description of face dataset Open test results considering imposters in the system Recognition rates considering only clients in the system Evolutionary design for face recognition Introduction Genetic algorithms Implementation Algorithm Representation and discrimination Whitening and rotation transformation Chromosome representation and genetic operators The fitness function The evolutionary pursuit algorithm for face recognition Frequency domain correlation filters in face recognition Introduction PSR calculation A brief review on correlation filters Mathematical background of a representative correlation filter ECPSDF filter design MACE filter design MVSDF filter design Optimal tradeoff (OTF) filter design Unconstrained correlation filter design UMACE filter design OTMACH filter design Physical requirements in designing correlation filters Applications of correlation filter in face recognition Performance analysis of correlation filters in face recognition Performance evaluation using PSR values Performance evaluation in terms of %RR and %FAR Performance evaluation by receiver operating characteristics (ROC) curves Correlation filters for face detection and recognition in video Formulation of unconstrained video filter Mathematical formulation of MUOTSDF Unconstrained video filter Distance classifier correlation filter Application of UVF in video for face detection Training approach Testing approach Face detection in video using UVF Validation of face detection Face classification using DCCF Subspace based face recognition in frequency domain Introduction Subspace based correlation filter Mathematical modelling of 1D subspace based correlation filters Reconstructed correlation filter using 1D subspace Optimum projecting image correlation filter using 1D subspace Face classification and recognition analysis in frequency domain Test results with 1D subspace analysis Comparative study in terms of PSRs Comparative study on %RR and %FAR Mathematical modelling of 2D subspace based correlation filter Reconstructed correlation filter using 2D subspace Test results on 2D subspace analysis PSR value distribution for authentic and impostors Comparative performance in terms of %RR Performance evaluation using ROC analysis Class specific subspace based nonlinear correlation filter Formulation of nonlinear correlation filters Nonlinear optimum projecting image correlation filter Nonlinear optimum reconstructed image correlation filter Face recognition analysis using correlation classifiers Test results Comparative study on discriminating performances Comparative performance based on PSR distribution Performance analysis using ROC Noise sensitivity Landmark localization for face recognition Introduction Elastic bunch graph matching Gabor Wavelets Gabor Jets The EBGM algorithm Application to face recognition Application of frequency domain correlation filter in facial landmark detection ASEF correlation filter Formulation of ASEF Eye detection Multi correlation approach using landmark filter for facial landmark detection Design of landmark filter (LF) Landmark localization with localization filter Test results Two dimensional synthetic face generation using set estimation technique Introduction Generating face points in the face space taking the features from intra class face images Face generation using algorithm with intra class features and related peak signal to noise ratio Generating face points in the face space taking the features from inter class face images Face generation with inter class features Rejection of the non-meaningful face and corresponding PSNR test Generalization capability of set estimation method: generating face images not in training set Test of significance Datasets of face images and performance tests for face recognition Face datasets ORL dataset OULU physics dataset XM2VTS dataset YALE dataset Yale-B dataset MIT dataset CMU pose, illumination, and expression (PIE) dataset UMIST dataset PURDU AR dataset FERET dataset Performance evaluation of face recognition algorithms FERET and XM2VTS protocols Face recognition grand challenge - FRGC Face recognition vendor test - FRVT Multiple biometric grand challenge Focus of evaluation Conclusion
Asit Kumar Datta is a former professor of the University of Calcutta (CU), Kolkata, India, where he served in the Department of Applied Physics and the Department of Applied Optics and Photonics. He holds an M.Tech and Ph.D from the same university. Dr. Datta spent 19 years as a professor and a total of 40 years of teaching and research at the post-graduate level at CU. In addition, he served for 8 years as a principal scientist/principal scientific officer of a CU research center in optical electronics. Widely published in international journals and conference proceedings, Dr. Datta has guided 14 scholars toward their Ph.Ds and has published nearly 125 papers. He has contributed significantly in the areas of photonic computation, photonic and electronic instrumentation, optical communications, and pattern recognition. He represented India at the International Commission on Optics and the International Commission on Illumination. Madhura Datta is the assistant director of the University Grants Commission-Human Resources Development Center, University of Calcutta, Kolkata, India. She holds an M.Sc in computer and information science, and an M.Tech and Ph.D in computer science and engineering from the University of Calcutta. Her primary areas of research are face detection and recognition. Her work has been featured in various technical publications and conference proceedings, including the Journal of Pattern Recognition Research, Computer Vision and Image Understanding, International Journal of Pattern Recognition and Artificial Intelligence, International Conference on Pattern Recognition and Machine Intelligence, and IEEE International Conference on Intelligent Human Computer Interaction. Pradipta Kumar Banerjee is an associate professor in the Department of Electrical Engineering of the Future Institute of Engineering and Management, Kolkata, India. He holds a B.Sc, B.Tech, M.Tech, and Ph.D from the University of Calcutta, Kolkata, India. His research interests include computer vision, image processing, frequency domain pattern recognition, biometrics, object detection, recognition, and tracking. He has been published in numerous technical journals, book chapters, and conference proceedings, such as Optics & Laser Technology, Optik - International Journal for Light and Electron Optics, Pattern Recognition Letters, Lecture Notes in Computer Science, 8th International Conference on Advances in Pattern Recognition, IEEE International Conference on Signal and Image Processing, IEEE Conference on Recent Advances in Intelligent Computational Systems, and many more.