Face Feature Extraction Based on Principle Discriminant Information Analysis

A general and efficient face feature extraction approach is presented which utilizes principle discriminant information of human faces. In order to get rid of redundant information and meanwhile reduce computational burden, we first compute the nonzero feature space of training set scatter matrix, and then perform a global search on it to seek out the most valuable discriminant information of faces. Genetic algorithm is used for searching because of its well-known global search ability. This strategy has achieved good performance in terms of recognition rate, computational cost and extended capability on ORL face database. Experiment shows this approach works much better than common used principle component analysis (PCA) method.