Face identification using affine simulated dense local descriptors

In this paper, we propose a method for pose and facial expression invariant face identification using the affine simulated local descriptors. Although the currently studied approaches present the higher recognition rate for face verification, the performance of face identification are still low. The proposed method consist of four step, we first normalize the face image using the face detector and eye detector. In second step, we apply the affine simulation for synthesizing various viewed face images. In third step, we make a descriptor on the overlapping block-based grid keypoints. In final step, a probe image is compared with the reference images in a gallery by calculating the number of nearest neighbor keypoints. To improve the recognition performance, we use also the keypoint distance ratio and false matched keypoint ratio. The proposed method using the affine simulated local descriptors showed the better performance than that of cosine similarity metric learning (CSML) method in terms of true acceptance rate, false rejection rate, false acceptance rate, and Rank-1 recognition rate.