A general discriminant model for color face recognition

This paper presents a General Discriminant Model (GDM)) for color face recognition. The GDM model involves two sets of variables: a set of color component combination coefficients for color image representation and a set of projection basis vectors for image discrimination. An iterative whitening maximization (IWM) algorithm is designed to find the optimal solution of the model. The proposed algorithm is further extended to generate three color components (like the three color components of RGB color images) for further improving the face recognition performance. Experiments using the Face Recognition Grand Challenge (FRGC) database and the Biometric Experimentation Environment (BEE) system show the effectiveness of the proposed model and algorithm. In particular, for the most challenging FRGC version 2 Experiment 4, which contains 12,776 training images, 16,028 controlled target images, and 8,014 uncontrolled query images, the proposed method achieves the face verification rate (ROC III) of 74.91 % at the false accept rate of 0. 1 %.