Face recognition by weighted multi-resolution uniform local Gabor binary patterns

In this paper, we present a method to face recognition which considers local shape information, weight of interesting region and texture information by Gabor filter, high-pass filter and local binary patterns, respectively. The face area can be largely divided into two dominant parts that one has high frequency domain and the other has low frequency domain. High frequency parts are interesting region which is edge of face shape, eye, nose, mouth and so on. Low frequency parts are forehead, cheek, and background beside face in the image. Then, the face image is divided into 6×5 small bins which represents a weight of the interesting region. Weight of interesting region is adopted for weighting of distance measure. Gabor filter can be easily extract shape features in face image. Local binary patterns are robust to various illumination conditions while Gabor transform is not. Therefore, we combined two features for robust features on various illuminations. For evaluating the illumination robustness of our face recognition system, we used the publicly available database POSTECH face 2007. Finally, we got 91.15% recognition rate by sixteen orientation of Gabor parameter + LBP + average Gaussian high-pass filter.