A Novel Null Space-Based Kernel Discriminant Analysis for Face Recognition

The symmetrical decomposition is a powerful method to extract features for image recognition. It reveals the significant discriminative information from the mirror image of symmetrical objects. In this paper, a novel null space kernel discriminant method based on the symmetrical method with a weighted fusion strategy is proposed for face recognition. It can effectively enhance the recognition performance and shares the advantages of Null-space, kernel and symmetrical methods. The experiment results on ORL database and FERET database demonstrate that the proposed method is effective and outperforms some existing subspace methods.

• Type: Conference
• Year: 2007
• Where: ICB
• Authors: Tuo Zhao, Zhizheng Liang, David Zhang, Yahui Liu
• Keywords: Biometrics, ICB 2007, Symmetrical, Symmetrical Decomposition, Symmetrical Methods