Facial feature extraction based on GSLDA

In this paper, a general and efficient facial feature extraction approach, global search linear discriminant analysis (GSLDA), is presented. It is designed to solve the puzzle of standard linear discriminant analysis (LDA) for small sample size problems (SSSP). Compared with PCA-LDA, in GSLDA, raw data dimension can be greatly decreased without discarding important discriminant information. In this process, all basis vectors of the non-null eigen-space of the scatter matrix is worked out, and then the well-known global search strategy, genetic algorithm, is enrolled to select basis vectors to construct a new feature space which has optimal discriminant ability. In contrast with PCA, this approach reserves more information for recognition. Therefore, this process enhances the performance of LDA for SSSP, and eventually the recognition performance. This strategy has been tested on the ORL and Yale face database. Experiment results show that this approach works much better than classical facial feature extraction methods.