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Research on Feature Extraction Algorithms in Face Recognition

Author: DuWenXia
Tutor: LiMing
School: Lanzhou University of Technology
Course: Signal and Information Processing
Keywords: Feature Extraction Principal Component Analysis Chaos Genetic Algorithm Discrete Cosine Transform Fuzzy linear discriminant analysis
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 100
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Abstract


Face recognition is an important branch of biometric identification , have broad application prospects in the field of document authentication, access control systems , video surveillance , criminal investigation detection . Feature extraction is a key step in face recognition . Extract effective discriminant features for face recognition system is of great significance . At present, the existing features of the various extraction theory and optimization methods or transformation method combining is an important research direction to achieve optimal facial feature extraction . In this paper, based on the combination of the characteristics of a variety of methods to extract theory , successful application of the new algorithm and feature extraction in face recognition . The main work of this paper include: (1) a chaos genetic algorithm and principal component analysis algorithm combining facial feature extraction method . The chaotic mapping chaos genetic algorithm using two different rules to maintain the diversity of the population , and enhance the global search ability of the algorithm . Eigenvectors to select the principal component analysis transformed by chaos genetic algorithm can quickly search in favor of classification feature subspace . Simulation ORL standard face database show that the method not only reduces the dimensionality of the feature space , but also better recognition performance than other methods . (2) is given based on the discrete cosine transform and fuzzy linear discriminant analysis combining facial feature extraction method . First dimensionality reduction and de-noising face image using the discrete cosine transform and fuzzy linear discriminant analysis , feature extraction transformed coefficient , minimum distance classifier for classification . The experiments on the ORL database show that the method to effectively filter out high frequency interference information in a face image , and enhance the ability to distinguish characteristics , to obtain better recognition results .

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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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