Weighted Complete Linear Discriminant Analysis and Its Application to Face Recognition
Xiaoguo Wang, Yong Qing Huang, Tieyong Cao, Xiongwei Zhang · 2008
In this paper, we propose a novel weighted complete linear discriminant analysis (WCLDA) method for feature extraction and recognition. The WCLDA first introduces a weighting function to restrain the dominant role of the classes with larger distance and then searches the optimal discriminant vectors under the conjugative orthogonal constrains in the null space of the within-class scatter matrix and its conjugative orthogonal complement space, respectively. As a result, the proposed technique derives the optimal and lossless discriminative information. Experiments on ORL and Yale face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of WCLDA.