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A Study on Face Recognition Base on Gabor Feature Using Supervised Locality Preserving Projection and Multi-Classifiers Fusion

Author: LiangWanJie
Tutor: ZhaoJianMin
School: Zhejiang Normal University
Course: Computer Software and Theory
Keywords: Face recognition Gabor wavelet transform SLPP algorithm Support vector machine Multi-classifiers fusion
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 138
Quote: 2
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Abstract


Face recognition is a focus of the research in the field of Pattern Recognition and Computer Vision which mainly includes face detection, facial feature extraction and facial feature classification. This paper discuss mainly on facial feature extraction and facial feature classification. This paper put forward a facial feature extraction algorithm which base on Gabor wavelet feature using supervised locality preserving projection, and a multi-classifiers fusion method which combine nearest feature line classifier and support vector machine classifier. The main contents of the paper can be noted as following:(1) At the facial feature extraction stage, this paper proposes one facial feature extraction algorithm which base on Gabor wavelet feature using supervised locality preserving projection and is abbreviated GSLPP. Due to the good characteristic of human face image’s Gabor wavelet feature, face recognition technology base on Gabor wavelet feature is a very popular method. However, human face image’s Gabor filter processing will increase the dimension of data. In order to reduce the dimension of data, this paper use supervised locality preserving projection algorithm to reduce the dimension of data. Locality preserving projection algorithm is a new subspace analysis method, which can preserve the most important part for face recognition. The method was successfully applied to face recognition in controlled environment. But the locality preserving projection algorithm is a non-supervised study method. When the human face image’s illumination, posture and expression changed, the recognition rate will decrease dramatically. For improving the recognition rate, this paper put forward a supervised locality preserving projection algorithm.(2) Based on the view that combining some kinds of classifier by some way maybe obtain higher recognition rate than a single one, this paper put forward a multi-classifiers fusion method which combine nearest feature line classifier and support vector machine classifier. The step of the multi-classifiers fusion method as following: firstly, using the nearest feature line classifier to pre-stage classify, if the result bigger than the defined threshold, then rejecting recognition, else pass to SVM classifier to classify. The multi-classifiers fusion method not only makes full use of the advantage which is high recognition rate of SVM classifier and high recognition speed of the nearest feature line classifier,but also utilizes the classification result of the nearest feature line classifier to guide the training and classification of SVM classifier.At last, the paper test this human face recognition method by some experiments on FERET Face Database and JDL-A Face Database. Those experiments illustrate that the method proposed by this paper is more effective than other methods, such as PCA、LDA and so on.

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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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