Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Face detection using quantized skin color regions merging andwavelet packet analysis
IEEE Transactions on Multimedia
A Transformation-Based Mechanism for Face Recognition
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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This paper proposes a face recognition method which is based on a Generalized Probabilistic Descent (GPD) learning rule with a three-layer feed-forward network. This method aims to recognize faces in a loosely controlled surveillance environment, which allows (1) large face image rotation (on and out of image plane), (2) different backgrounds, and (3) different illumination. Besides, a novel light compensation approach is designed to compensate the gray-level differences resulted from different lighting conditions. Experiments for three kinds of classifiers (LVQ2, BP, and GPD) have been performed on a ITRI face database. GPD with the proposed light compensation approach displays the best recopition accuracy among all possible combination.