Linear discriminant analysis (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. Some approaches have been proposed to overcome this problem, but they are often unstable and have to discard some discriminative information. In this paper, a dual-space LDA approach for face recognition is proposed to take full advantage of the discriminative information in the face space. Based on a probabilistic visual model, the eigenvalue spectrum in the null space of within-class scatter matrix is estimated, and discriminant analysis is simultaneously applied in the principal and null subspaces of the within-class scatter matrix. The two sets of discriminative features are then combined for recognition. It outperforms existing LDA approaches.
Dual-space linear discriminant analysis for face recognition
2004-01-01
264680 byte
Conference paper
Electronic Resource
English
Dual-Space Linear Discriminant Analysis for Face Recognition
British Library Conference Proceedings | 2004
|Robust pose invariant face recognition using coupled latent space discriminant analysis
British Library Online Contents | 2012
|Face recognition using discriminant locality preserving projections
British Library Online Contents | 2006
|Complete discriminant evaluation and feature extraction in kernel space for face recognition
British Library Online Contents | 2009
|Discriminant analysis with Gabor phase feature for robust face recognition
British Library Online Contents | 2013
|