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Full Description
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 andexplains 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®/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.
Contents
Introduction. Face detection and recognition techniques. Subspace based face recognition. Face detection by Bayesian approach. Face detection in colour and infrared images. Intelligent face detection. Real time face detection. Face space boundary selection for face detection and recognition. Evolutionary design for face recognition. Frequency domain correlation filters in face recognition. Subspace based face recognition in frequency domain. Landmark localization for face recognition. Two dimensional synthetic face generation using set estimation technique. Datasets of face images and performance tests for face recognition.
