详情

原文传递 Facial Recognition Algorithm Comparison: Using a Hybrid EigenFace ARTMAP Neural Network vs. the Tracking-Learning-Detection (TLD) Algorithm.

题名：	Facial Recognition Algorithm Comparison: Using a Hybrid EigenFace ARTMAP Neural Network vs. the Tracking-Learning-Detection (TLD) Algorithm.
作者：	Kelley, T. D.; McGhee, S.; Avery, E.
关键词：	Computer vision, Facial recognition, Tracking, Robotics, Neural nets, Factor analysis, Tld (tracking-learning-detection), Tld algorithm, Hybrid ea neural network, Ea (eigenface artmap), Ss-rics (subsymbolicrobotics intelligence control system), Pca (principal component analysis), Art (art adaptive resonance theory), Roi (roi region of interest)
摘要：	This report describes a comparison of two facial recognition processes for continuous learning. One process used an ARTMAP neural network with features extracted using a modified EigenFace implementation. This was compared with training the Tracking-Learning-Detection (TLD) algorithm using faces from a television episode. Results indicated that the TLD algorithm was superior to the Hybrid EigenFace/ARTMAP (EA) for the entire episode but that the Hybrid EA algorithm was better for the second half of the episode. The ARTMAP was chosen because it can adaptively train to new vectors without suffering from catastrophic forgetting. However, the TLD algorithm was capable of better online learning and overall performance.
报告类型：	科技报告