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About this product
- Author(s)Asit Kumar Datta,Madhura Datta,Pradipta Kumar Banerjee
- PublisherApple Academic Press Inc.
- Date of Publication04/01/2016
- GenreComputing: Professional & Programming
- Place of PublicationOakville
- Country of PublicationCanada
- ImprintApple Academic Press Inc.
- Content Note137 black & white illustrations, 28 black & white tables
- Weight816 g
- Width156 mm
- Height235 mm
- Spine25 mm
- Format DetailsUnsewn / adhesive bound
- Table Of ContentsIntroduction 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
- Author BiographyAsit 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.
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