Research And Application Of Face Recognition Algorithm Based On Feature Fusion

As the security threats continue to increase, in sensitive site the installation of high reliableand efficiency identification system becomes very important. Biometric identification for its reliability, uniqueness and stability has become a hot area of scholars research. It is widely used in access control, certificate verification, the judicial application and other fields.Compared with other biological characteristics, the characteristics of the human face is one of the most natural, so face recognition has been widely applied in identity authentication, public safety, and has a wide market foreground. But because the face image is easily influenced by facial expression, illumination, age, noise, occlusion hair or glasses and other factors, so the facial feature extraction of good distinction and high robustness, the design of the high-performance classifier and the improved recognition rate has been the focus in the field of face recognition and challenges. Because Gabor filter can capture the local characteristics of face in different scales and different directions, and it is robust to illumination changes and facial gestures, so it is widely used in face recognition system. According the Gabor feature in the process of face recognition and the design of classifier, this paper makes a thorough study. The main work is as follows:(1)According to geometric distributions of facial organs, based on the principle of Haar-Like features, this paper proposes two Haar-Like_F features, and according to the edge characteristics of human face after rotation, fuses the triangle features with the Haar-like_F features. First extract the Haar-Like_F features, triangle features of face images, then fuse the two features, then fused features, Haar-Like_F features and original Haar-Like features are all input into AdaBoost algorithm to generate weak classifiers for feature selection. Finally cascade those strong classifiers for face detection. The experimental result is conducted on OpenCv which is an open source vision database, it proves the effectiveness of the proposed algorithm compared with the original Haar-Like algorithm.(2)Considering that the human face feature extraction of high dimension of deficiencies based on traditional Gabor wavelet transform, this paper proposes an improved Gabor feature fusion and SVM face recognition algorithm, and it accelerates to solve with the two-dimensional Fourier transform and improves feature extraction rate. First extract the Gabor multi-directional and multi-scale features of face image, and then fuse the features in the same direction at different scales. Second reduce the fusion feature dimension by fastPCA algorithm, and finally identify and classify the face with the improved SVM classifier, then fuse the classifier by using the two kinds of processing pattern, the experimental results are conducted on ORL face database, the results show that the algorithm can effectively characterize face and improve recognition rate.(3)Considering that the high dimensional deficiencies of face feature extraction based on traditional Gabor wavelet transform and low recognition rate caused by lack of training samples, this paper proposes a face recognition algorithm based on modular Gabor feature fusion,and it classifies the face images by introducing the membership function.First extract the Gabor multi-directional and multi-scale features of face images,and then fuse the features in the same direction at different scales,and divide Gabor feature faces into chunks according to the importance in order to make full use of the contribution of facial organs to face recognition, and finally classify the face images by introducing the membership function.The experimental result is conducted on standard face databases, it shows that the algorithm can effectively improve recognition rate when the training samples are insufficient and improve the performance of face recognition system.Through experiment, the results show that the algorithm of this paper can effectively improve recognition rate and the performance of face recognition system, at the same time it has the very good practical value. Finally the algorithm in this paper is preliminary applicated to the actual, the results show that the algorithm has good performance.