Face recognition using feature combination and improved LDA

Abstract

A personalized feature framework based on improved linear discriminant analysis (LDA) is proposed for face recognition (FR). In traditional LDA, the definition of the between-class matrix might cause large overlaps of neighbouring classes, so we add a weighting function into the between-class scatter matrix. In the framework, the improved LDA makes use of the null space the within-class scatter matrix effectively, and global feature vectors and local feature vectors are integrated by complex vectors as input features of improved LDA. The proposed method is compared to other commonly used FR methods on two face databases. Results demonstrate that the proposed method outperforms traditional FR approaches.