Dual-space linear discriminant analysis for face recognition

TLDR

Linear discriminant analysis is widely used for face recognition but suffers from the small‑sample‑size problem, and existing remedies often discard discriminative information. This study introduces a dual‑space LDA method that fully exploits discriminative information in face data. By estimating the eigenvalue spectrum in the null space of the within‑class scatter matrix with a probabilistic visual model, the method applies discriminant analysis simultaneously in the principal and null subspaces and then combines the resulting feature sets. The dual‑space LDA outperforms existing LDA approaches in recognition accuracy.

Abstract

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.