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Feature-Based Improved Face Recognition by Gabor Wavelet

Author: YuYan
Tutor: LiZhengMing
School: Jiangsu University
Course: Control Theory and Control Engineering
Keywords: Face Recognition Gabor wavelet SFFS Block sampling Features restructuring PCA SVM
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 282
Quote: 5
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Abstract


Face recognition technology is a pattern recognition and machine vision of the most challenging research topics , it is in public safety , information security, human-computer interaction and other fields have a wide range of applications. In general, the face recognition system including face detection, feature point positioning, image preprocessing, feature extraction and face recognition . In the previous article , based on research related issues , mainly to do the following tasks: ( a ) first introduces the research background and significance of face recognition , face recognition technology development course outlines , as well as in the domestic research and development outside the status quo . Then constructed from the face recognition system to start, discuss the whole recognition process in all the major steps of the various aspects of the technology required , the algorithm was introduced and studied. ( 2 ) In the face feature extraction stage , Gabor wavelet transform for two-dimensional high dimension feature set , the recognition time can not meet the real requirements of the problem , this paper proposes an improved algorithm : First, use the block sampling and sequential floating forward France sampling points for feature selection , and then Gabor face representation set of features for feature recombination 40 characteristic matrix , and using principal component analysis (PCA) algorithm to reduce the dimension noise reduction , and finally by constructing support vector machines (SVM ) classifier to recognize human faces . ( 3 ) By ORL and Yale face database on experimental evidence , with the traditional two-dimensional Gabor wavelet -based algorithm, speed has been significantly improved recognition , face recognition and the ability to effectively solve the \rate is not high. This feature extraction algorithm is also used during the PCA and SVM algorithm parameter selection has good robustness , this feature not only to a certain extent to solve the SVM kernel parameter selection difficult problem , but also makes the application of the algorithm broader scope .

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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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