Connectionist models of face processing: A survey
作者： Dominique Valentin , Hervé Abdi , Alice J. O'Toole , Garrison W. Cottrell
DOI: 10.1016/0031-3203(94)90006-X
关键词: Categorization 、 Machine learning 、 Backpropagation 、 Artificial neural network 、 Feature selection 、 Pattern recognition (psychology) 、 Artificial intelligence 、 Face (geometry) 、 Facial recognition system 、 Connectionism 、 Computer science
摘要: Abstract Connectionist models of face recognition, identification, and categorization have appeared recently in several disciplines, including psychology, computer science, engineering. We present a review these with the goal complementing recent survey by Samal Iyengar [Pattern Recognition25, 65–77 (1992)] nonconnectionist approaches to problem automatic recognition. concentrate on that use linear autoassociative networks, nonlinear (or compression) and/or heteroassociative backpropagation networks. One advantage over some is analyzable features emerge naturally from image-based codes, hence feature selection segmentation faces can be avoided.
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