A Novel Cross Iterative Selection Method for Face Recognition - Dai, Xiuli; Chen, Wen-Sheng; Pan, Binbin; Chen, Bo (Biometric Recognition, 2014)
To enhance the discriminant power of features in face recognition, this paper builds a novel discriminant criterion by nonlinearly combining global feature and local feature, which also incorporates the geometric distribution weight information of the training data. Two formulae are theoretically derived to determine the optimal parameters that balance the trade-off between global feature and local feature. The obtained parameters automatically fall into interval [0, 1]. Based on the parameter formulae, we design an efficient cross iterative selection (CIS) algorithm to update the optimal parameters and optimal projection matrix. The proposed CIS approach is used for face recognition and compared with some existing methods, such as LDA, UDP and APD methods. Experimental results on the ORL and FERET databases show the superior performance of the proposed algorithm.