Face Recognition Approaches Based On Circularly Symmetrical Gabor Transforms

As a research hotspot of computer vision and pattern recognition, face recognition technology is a research topic which has important theory significance and application value, which involves the content of artificial intelligence, image processing and neural network etc. With the development of the society and the progress of science and technology, the demand for face recognition technology in society increases constantly, and the application prospects of accurate and fast face recognition technology become more and more broad. Face recognition is a complex and challenging research subject, where the complexity comes from two aspects:.on one hand the change of the face itself, such as the change of facial gestures and expressions, and changes caused by different ages, on the other hand, the change of the background, such as the change of illumination.The key part of face recognition is feature extraction. Good feature extraction technology must meet the following two points:① strong robustness of face recognition approaches to the variable factors which include illumination, expression, poses and the rotation;② low computational complexity. This thesis mainly studies face feature extraction. Compared with the traditional Gabor wavelet transform, the circularly symmetric Gabor transform (CSGT) has the advantages of low redundancy and strict rotation invariance, so CSGT is selected as the method of feature extraction in this thesis.The main works we have done include what follows. Sophisticated study is conducted to CSGT, and several multi-scale feature fusion schemes are proposed. Study is conducted to the template of CSGT. New face recognition methods are proposed based on CSGT. First, face images are mapped onto the CSGT domain and the feature images are constructed by using multi-scale feature fusion scheme in the same domain, then the features are further extracted by using subspace method, finally, classifier is used to achieve classification.The main work of this thesis is as follows:(1) In-depth research is conducted on CSGT:different influences of different parameters on CSGT are analyzed; 5 multi-scale feature fusion schemes are proposed and three fusion schemes which have good recognition effect are chosen to conduct experiment.(2) A new face recognition method based on CSGT and PCA+SVM is proposed. The face images are mapped onto the CSGT domain and the feature images are constructed by using multi-scale feature fusion scheme first. Then the features are further extracted by using PCA method. Finally, SVM is applied to achieve classification. The fusion schemes used in this algorithm are average figure method and maximum figure method respectively. After fusion, the size of the feature image is the same as that of the original image. Experiments on ORL database and FERET database are carried out. The experimental results show the effectiveness of the multi-scale fusion features and the feasibility of the proposed method.(3) Study on the variable template CSGT is conducted and experiments based on fixed template CSGT and variable template CSGT are performed respectively.(4) Two new approaches based on CSGT and 2DPCA are proposed, which are CSGT+2DPCA+NN and CSGT+2DPCA+SVM respectively. In this part,2DPCA method is used to extract classification features. In the CSGT+2DPCA+NN approache, experiments based on fixed template CSGT and variable template CSGT are performed respectively.CSGT+2DPCA+NN face recognition method:According to different fusion scheme, three methods based on variable template CSGT are put forward, which are *CSGT1+2DPCA, *CSGT2+2DPCA and *CSGT3+2DPCA, correspond to 3 different fusion schemes:maximum figure method, average figure method and images of 5 scales mosaic algorithm. Since the maximum figure method has the best recognition results, the method CSGT1+2DPCA based on fixed template CSGT are proposed too. Experiments on ORL database and FERET database are carried out.The experimental results show that the four methods proposed in this thesis can all achieve better recognition results than existing approaches.CSGT+2DPCA+SVM face recognition method:In this algorithm, the Support Vector Machine (SVM) is used as the classifier. Comparative experiments with CSGT+PCA+SVM indicate that the CSGT+2DPCA+SVM method can save calculation time and have higher recognition rates.