Random Sampling for Face Recognition
LDA is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address this problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. In this paper, by analyzing different overfitting problems for the two kinds of LDA classifiers, we propose an approach using random subspace and bagging to improve them respectively. By random sampling on feature vector and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed. The two kinds of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information are preserved. We also apply this approach to the integration of multiple features. A robust face recognition system integrating shape, texture, and Gabor responses is finally developed.
Inventor: Tang, Xiao-ou
Priority Number: CN100356387C
IPC Current: G06K000900
Assignee Applicant: The Chinese University of Hong Kong
Title: Face recognition method based on random sampling | Based on face identifying method of random sampling
Usefulness: Face recognition method based on random sampling | Based on face identifying method of random sampling
Novelty: Face recognition method based on random sampling
ICT/Telecom
Image Processing
04/ENG/182
Inventor(s): Professor Xiaoou TANG, Department of Information Engineering
Licensing Status: Available for licensing
Patent Status: Chinese Patent Pending Hong Kong Patent Pending
Hong Kong
