Enhanced Discrimination of Face Orientation Based on Gabor Filters

In general, a face analysis relies on the face orientation; therefore, face orientation discrimination is very important for interpreting the situation of people in an image. In this paper, we propose an enhanced approach that is robust to the unwanted variation of the image such as illumination, size of faces, and conditions of picture taken. In addition to the conventional algorithm (Principal Component Analysis and Independent Component Analysis), we imposed the Gabor kernels and Fourier Transform to improve the robustness of the proposed approach. The experimental results validate the effectiveness of the proposed algorithm for five kinds of face orientation (front, quarter left, perfect left, quarter right, and perfect right side of faces). In real application, the proposed algorithm will enable a Human-Computer Interface (HCI) system to understand the image better by extracting reliable information of face orientation.