Color HOG-EBGM for face recognition

Most face recognition algorithms make only use of intensity information of the images discarding color as a distinctive cue. This paper extends the HOG-EBGM face recognition algorithm to use color information. In HOG-EBGM, each face is represented by a graph described by HOG features at specific landmarks. The color extension of the method here proposed intends to make the algorithm robust against color changes, while keeping its former robustness against scale, position, rotation and intensity variations. In the paper, several color representations of the faces are studied. Also, to reduce the higher dimensionality of the new color features, a comparison of dimensionality reduction techniques is included. Results on the Experiment 4 of the Face Recognition Grand Challenge show that color HOG-EBGM outperforms the gray-scale version of the algorithm in all cases. The best results were obtained using the Opponent color space with LDA.