Human Facial Feature Matching based on Motion-Smoothness Constraint

Abstract

Face recognition is a practical and important problem. In our life, face recognition problems exist everywhere. How to identify the face in the image accurately and quickly is an important and urgent topic. In this paper, an efficient and accurate face recognition method based on keypoints feature matching is proposed to extract the keypoints features of the face image, and then the nearest neighbor method is used to obtain a large number of keypoints rough matching. Then the correct matching of the keypoints is determined by the motion smoothness constraint of the original keypoints matching. Finally, the matching of the face is determined according to the number of keypoints matching between the two input face images. Some experiments show that this method is suitable for face recognition with expression change and pose change.