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Research on Face Recognition Based on Binding Feature Extraction and Support Vector Machines

Author: NingZhiGang
Tutor: XieHong
School: Harbin Engineering University
Course: Signal and Information Processing
Keywords: Face Recognition Support Vector Machine Optimal hyperplane Kernel function Dimensional Principal Component Analysis
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 52
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The face recognition technology is the use of computer analysis of face images to extract the effective identification information identifying or discriminating a pending state technology . It involves knowledge of pattern recognition , image processing , computer vision , physiology , psychology and many other disciplines , is currently one of the hot spots of pattern recognition and artificial intelligence research , analysis support vector machine method ( SVM ) face recognition application The basic idea of ??the theory of statistical learning theory and support vector machine . Analysis of several classic multi - classification algorithm : one-to-many algorithm , one-on-one algorithm , error correction ended output encoding method and an implicit decomposition strategy algorithm ; analysis of the factors that affect the classification results ; compared several algorithms technical characteristics , select OAO as classification algorithm for face recognition system . Then , the analysis of the two feature extraction algorithms : principal component analysis based on KL transform algorithm (PCA) and two - dimensional principal component analysis algorithm ( 2DPCA ) . PCA feature extraction method is statistically optimal , but the image matrix into higher dimension of the image vector after consuming large amount of computing throughout the feature extraction process . 2DPCA image matrix - based , image feature extraction is simple and intuitive , but in the process of image reconstruction requires more collaborative factors and coefficients . This paper presents an improved feature extraction algorithm that 2DPCA-PCA combined algorithm to further reduce the number of feature dimensions , feature extraction faster , better classification . Finally, the establishment of 2DPCA-PCA - based face recognition and support vector machine simulation system . Simulation based on the ORL and combo face library , compared PCA combined with KNN ( nearest neighbor classifier ) system the the 2DPCA - PCA joint KNN , PCA Joint SVM system , 2DPCA-PCA Joint SVM system . Than KNN classifier , SVM classifier has good generalization , classification accuracy ; of 2DPCA - - PCA the United SVM face recognition system compared to other system recognition speed and recognition rate .

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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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