Efficient feature selection for linear discriminant analysis and its application to face recognition

Feature selection is an important issue in pattern recognition. In face recognition, one of the state-of-the-art methods is that some feature selection methods (e.g., AdaBoost) are first utilized to select the most discriminative features and then the subspace learning methods (e.g., LDA) are further applied to learn the discriminant subspace for classification. However, in these methods, the objective of feature selection and subspace learning is not so consistent and the combination is not the optimal. In this paper, we propose a novel and efficient feature selection method that is designed for linear discriminant analysis (LDA). We use the Fisher criterion to select the most discriminative and appropriate features so that the objectives of feature selection and classifier learning are consistent (both follow the Fisher criterion) and the face recognition performance is expected to be improved. Experiments on FRGC v2.0 face database validate the efficacy of the proposed method.