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Research on Face Recognition Method Based on Statistical Theory

Author: XuDongDong
Tutor: ChenXiuHong
School: Jiangnan University
Course: Applied Computer Technology
Keywords: Face Recognition GLRAM LDA Image Block Feature Extraction Block 2D Preserving Projections
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 184
Quote: 2
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Abstract


The main task of pattern recognition is the use of the characteristics of the sample , the sample is divided into the category of the corresponding mode . Here feature extraction is the key aspects of face recognition , face feature extraction method not only helps to simplify the the subsequent classifier design and to improve the recognition rate . The face recognition feature extraction and description can be divided into two categories based on the geometric characteristics based on statistical characteristics . Early face recognition research is mainly based on the geometric characteristics , proposed in recent years , most of the methods are based on statistical characteristics . Face recognition method based on the statistical characteristics of the main template matching , subspace method , support vector machines , the article focuses on the subspace method , the main tasks : 1 ) based on the statistical characteristics of GLRAM ( matrix generalized low rank approximation ) and LDA ( linear discriminant analysis ) method proposed combination one GLRAM with LDA face recognition method . The the First GLRAM method effective features for face image , then characteristics obtained by LDA dimensionality reduction and optimal classification feature . Extract features which makes the judgment has been significantly enhanced. The experimental results show that the algorithm achieved high recognition rate within a short period of time , and is better than the traditional method of GLRAM . 2 ) two-dimensional projection method of preserving a two-dimensional image block block Preserving Projections ( block 2DLPP) method , and successfully used for face recognition . The method first block of the original image matrix , then partitioned sub- image purposes 2DLPP method , image dimensionality reduction . This method can effectively extract the local features of an image . The experiments show that the recognition performance superior 2DLPP method .

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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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