Features Selection and GMM Classifier for Multi-Pose Face Recognition

• Main Authors: Ding-En Wang, 王鼎恩
• Other Authors: Shin-Feng Lin
• Format: Others
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/03408850317662581389

碩士 === 國立東華大學 === 資訊工程學系 === 103 === Face recognition is widely used in security application, such as homeland security, video surveillance, law enforcement, and identity management. However, there are still some problems in face recognition system. The main problems include the light changes, facial expression changes, pose variations and partial occlusion. Although many face recognition approaches reported satisfactory performance, their successes are limited to the conditions of controlled environment. In fact, pose variation has been identified as one of the most current problems in the real world. Therefore, many algorithms focusing on how to handle pose variation have received much attention. To solve the pose variations problem, in this thesis, we propose a multi-pose face recognition system based on an effective design of classifier using SURF feature. In training phase, the proposed method utilizes SURF features to calculate similarity between two images from different poses of the same face. Face recognition model (GMM) is trained using the robust SURF features from different poses. In testing phase, feature vectors corresponding to the test images are input to all trained models for the decision of the recognized face. Experiment results show that the performance of the proposed method is better than other existing methods.