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Research on Based on Singular Value Decomposition and KL Projection Fusion Decision

Author: YangMu
Tutor: ZhouJiLiu；LuMinChun
School: Sichuan University
Course: Software Engineering
Keywords: Face Recognition KL algorithm SVD decomposition Fisher linear discriminant Feature Fusion
CLC: TP391.41
Type: Master's thesis
Year: 2004
Downloads: 334
Quote: 8
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Abstract


Face recognition is a difficult field of pattern recognition research topic that has practical value and broad application prospects . It as a good biometric technology is one of the most challenging issues in the field of machine vision and pattern recognition . The face recognition technology, such as banking systems , military security , security checks have considerable application prospects . Face recognition method based on algebraic characteristics did some research , a fusion of face recognition based on the KL projection and singular value decomposition method in existing PCA face recognition algorithm based on the proposed . Firstly, by KL transform to obtain images of the main feature vectors ; singular value decomposition singular value feature vector followed by images of human faces , followed by two Fisher linear optimization : Finally, the main KL projection feature vectors and SVD the eigenvectors phase fusion to the formation of the new feature vector , and as a criterion . The method not only has the KL projection algorithm to identify the correct rate , but also has the important characteristics of the singular value projection algorithm to describe the image showed transposition invariance , rotational invariance , displacement invariance . Both organic fusion , eliminating the correlation between the decline in the face image recognition accuracy when the attitude change of the human face detection library , and greatly improve the correct rate of face recognition . By using this method can be seen that the method of testing and analysis of the integration of face recognition system based on the the KL projection and singular value decomposition than used alone the KL projection algorithm and used alone SVD eigenvectors projection algorithm has better classification feature , be able to Get more satisfied with the results of face recognition .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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