Multi-pose Face Recognition Based on Improved ORB Feature

To overcome the weakness of multi-pose face recognition by global feature or by single template sample for each subject, a novel multi-pose face recognition method based on improved oriented FAST and rotated BRIEF(ORB) local feature and multiple template samples for each subject is proposed in this paper. The sampling pattern of ORB operator is first improved to enhance the robustness of the operator to the variation of viewpoint towards face, and template database is built with multiple training samples of each subject, the improved ORB features of the test sample are next extracted and matched with those of template samples. At the stage of feature extraction, consistent number of keypoints are extracted for all samples to avoid the disturbance of the variation of the number of keypoints. At the stage of feature matching, double strategies based on the model and orientation of matching-point pairs are adopted to eliminate outliers. The consistent degree of test sample and each template sample is evaluated by the number and average distance of the inliers. Experimental results on the CAS-PEAL-R1 and XJTU databases show that, the improved ORB operator has better recognition performance; compared with the methods of constructing single template sample from multiple training samples for each subject, the proposed method could better avoid the disturbance of pose variation, and obtain better recognition results under the condition of using the same number of training samples. Compared with scale invariant feature transform(SIFT) operator, the ORB operator has obvious advantages in speed at both stages of feature extraction and feature matching.