Multiple Face Model of Hybrid Fourier Feature for Large Face Image Set

Abstract

The face recognition system based on the only single classifier considering the restricted information can not guarantee the generality and superiority of performances in a real situation. To challenge such problems, we propose the hybrid Fourier features extracted from different frequency bands and multiple face models. The hybrid Fourier feature comprises three different Fourier domains; merged real and imaginary components, Fourier spectrum and phase angle. When deriving Fourier features from three Fourier domains, we define three different frequency bandwidths, so that additional complementary features can be obtained. After this, they are individually classified by Linear Discriminant Analysis. This approach makes possible analyzing a face image from the various viewpoints to recognize identities. Moreover, we propose multiple face models based on different eye positions with a same image size, and it contributes to increasing the performance of the proposed system. We evaluated this proposed system using the Face Recognition Grand Challenge (FRGC) experimental protocols known as the largest data sets available. Experimental results on FRGC version 2.0 data sets has proven that the proposed method shows better verification rates than the baseline of FRGC on 2D frontal face images under various situations such as illumination changes, expression changes, and time elapses.