Research On The Application Of Image Processing In Face Recognition

Most recently, as the most natural and explicit approach for biological feature recognition, face recognition has attracted increasing attentions. Although face recognition approaches based on the visual light spectrum have gained some success,it not only unsuitable for the disguised face, but also the performance may be affected by the change of the lighting, especially when the lighting is uneven or dim, or in the outdoor circumstances, the recognition rate may decline obviously. Face recognition system based on infrared (IR) have better performance in terms of robustness.However, when the people put on the glasses, the performance of system recognition shows a shape decrease. Based on all the issues mentioned above, in the thesis, the study focus on multi spectral image fusion technology that is utilized to improve the overall performance of the Face recognition system. the image slicing technology based on singular value decomposition (SVD) is used to the fusion of multi-pattern image, then the fusion image is used to recognize the faces based on the principal component analysis (PCA) method.(1)Approach of normalization for face image the is studied. Firstly, gray normalization of the face image is realized, then the eye location process is carried out, the standard face is obtained by using geometric normalization the based on the position information of the eyes.(2)Further, the singular value decomposition (SVD) of and the face recognition algorithm based on PCA are studied. Principles and applications of the SVD for the face image are introduced, the method of obtaining features of face and steps for face recognition based on PCA are illustrated as well.(3)Image slicing approaches is utilized to realize the fusion of visible and light infrared based on the energy of the image. Firstly, the SVD decomposition of layers according to different energy , i.e., low, high and ultrahigh resolution layers ,respectively. Besides, in each layer, the fusion strategies is used according to the different performance features. Last, the face recognition algorithm is realized through the fusion of the image. In the thesis, several experimental results of fusion algorithm are compared, the effectiveness of the algorithm is verified as well.