SEGMENTATION OF MULTIPLE HUMAN OBJECTS IN VIDEO SEQUENCES
Applied Artificial Intelligence
Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition
International Journal of Computer Vision
A multiexpert collaborative biometric system for people identification
Journal of Visual Languages and Computing
Normal maps vs. visible images: Comparing classifiers and combining modalities
Journal of Visual Languages and Computing
A direct evolutionary feature extraction algorithm for classifying high dimensional data
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Evolutionary discriminant feature extraction with application to face recognition
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Face recognition in global harmonic subspace
IEEE Transactions on Information Forensics and Security
Weighted principal component extraction with genetic algorithms
Applied Soft Computing
Gabor feature based classification using LDA/QZ algorithm for face recognition
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Parsimonious feature extraction based on genetic algorithms and support vector machines
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Discriminant phase component for face recognition
Journal of Electrical and Computer Engineering
Computers and Electronics in Agriculture
Hi-index | 0.00 |
This paper addresses the dimension reduction problem in Fisherface for face recognition. When the number of training samples is less than the image dimension (total number of pixels), the within-class scatter matrix (Sw) in linear discriminant analysis (LDA) is singular, and principal component analysis (PCA) is suggested to employ in Fisherface for dimension reduction of Sw so that it becomes nonsingular. The popular method is to select the largest nonzero eigenvalues and the corresponding eigenvectors for LDA. To attenuate the illumination effect, some researchers suggested removing the three eigenvectors with the largest eigenvalues and the performance is improved. However, as far as we know, there is no systematic way to determine which eigenvalues should be used. Along this line, this paper proposes a theorem to interpret why PCA can be used in LDA and an automatic and systematic method to select the eigenvectors to be used in LDA using a genetic algorithm (GA). A GA-PCA is then developed. It is found that some small eigenvectors should also be used as part of the basis for dimension reduction. Using the GA-PCA to reduce the dimension, a GA-Fisher method is designed and developed. Compared with the traditional Fisherface method, the proposed GA-Fisher offers two additional advantages. First, optimal bases for dimensionality reduction are derived from GA-PCA. Second, the computational efficiency of LDA is improved by adding a whitening procedure after dimension reduction. The Face Recognition Technology (FERET) and Carnegie Mellon University Pose, Illumination, and Expression (CMU PIE) databases are used for evaluation. Experimental results show that almost 5% improvement compared with Fisherface can be obtained, and the results are encouraging.