| Titre : | Face Detection and Recognition Theory and Practice | | Type de document : | texte imprimé | | Auteurs : | Asit Kumar Datta ; Madhura Datta ; Pradipta Kumar Banerjee | | Editeur : | CRC Press | | Année de publication : | 2016 | | Importance : | 325 p | | Présentation : | couv. ill. | | Format : | 24*16 cm | | ISBN/ISSN/EAN : | 978-1-482-22654-6 | | Note générale : | Index; Bibliogr.(305-3321)p | | Langues : | Français (fre) | | Mots-clés : | Face Detection Recognition | | Index. décimale : | 621.381 DAT | | Résumé : | This book discusses the major approaches, algorithms, and technologies used in automated face detection and recognition. Explaining the theory and practice of systems currently in vogue, the text 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, face recognition in frequency domain, and more. It features pictorial descriptions of every algorithm as well as downloadable source code (in MATLAB ® /PYTHON) and hardware implementation strategies with code examples. Key Features  Explains the theory and practice of face detection and recognition systems currently in vogue  Offers a general review of the available face detection and recognition methods, as well as an indication of future research using cognitive neurophysiology  Provides a single source for cutting-edge information on the major approaches, algorithms, and technologies used in automated face detection and recognition Selected 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. |
Face Detection and Recognition Theory and Practice [texte imprimé] / Asit Kumar Datta ; Madhura Datta ; Pradipta Kumar Banerjee . - [S.l.] : CRC Press, 2016 . - 325 p : couv. ill. ; 24*16 cm. ISBN : 978-1-482-22654-6 Index; Bibliogr.(305-3321)p Langues : Français ( fre) | Mots-clés : | Face Detection Recognition | | Index. décimale : | 621.381 DAT | | Résumé : | This book discusses the major approaches, algorithms, and technologies used in automated face detection and recognition. Explaining the theory and practice of systems currently in vogue, the text 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, face recognition in frequency domain, and more. It features pictorial descriptions of every algorithm as well as downloadable source code (in MATLAB ® /PYTHON) and hardware implementation strategies with code examples. Key Features  Explains the theory and practice of face detection and recognition systems currently in vogue  Offers a general review of the available face detection and recognition methods, as well as an indication of future research using cognitive neurophysiology  Provides a single source for cutting-edge information on the major approaches, algorithms, and technologies used in automated face detection and recognition Selected 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. |
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