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

Linear discriminant analysis subspace methods for face recognition

Author WangZengFeng
Tutor WangHuiYuan
School Shandong University
Course Signal and Information Processing
Keywords Face recognition feature extraction subspace analysis Fisher linear discriminant analysis fractional-step linear discriminant analysis
CLC TP391.41
Type Master's thesis
Year 2006
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The technology of face recognition is an active subject in the area of pattern recognition. There are broad applications in the fields of law, business etc. Face recognition includes three parts: face detection and localization, feature extraction and classification. This thesis presents the studies of feature extraction in face recognition.Subspace analysis is one of the most popular methods of feature extraction. We will introduce various subspace analysis methods and their applications in face recognition. By comparing these subspace analysis methods in theory and experiment results, we point out that linear discriminant analysis is more suitable for pattern recognition than other subspace methods.Fisher linear discriminant analysis has obtained successful applications in face recognition. A new LDA-based face recognition algorithm is proposed in the thesis which is called "weighted null space approach". Due to the fact that Fisher criteria is not directly related to the classification accuracy, we propose a strategy to compute the total between-class scatter matrix and the pooled within-class scatter matrix using some weight functions. A weighted total between-class scatter matrix is constructed in which smaller distances are more heavily weighted than larger distances, because those classes which are clustered together are more likely to be misclassified; a weighted pooled within-class scatter matrix is constructed in which outlier class is more lightly weighed, because if one class is well separated from the other classes in the input space ,then whether the within-class covariance matrix of this class in the new space is compact or not will not have much influence on the classification result. We test this algorithm on ORL and FERET human face databases. Experiment results show that weighted null space method is an excellent face recognition method. It can achieve a high classification accuracy by reserving only a few feature vectors.We also introduce an iterative linear discriminant analysis-fractional linear discriminant analysis to face recognition. A new feature extraction method of face recognition which combines principal component analysis and fractional-step linear

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