| Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier |
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| 作者姓名： | 张燕昆 刘重庆 |
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| 作者单位： | Zhang Yankun & Liu Chongqing Institute of Image Processing and Pattern Recognition，Shanghai Jiao long University，Shanghai 200030 P.R.China |
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| 基金项目： | This project was supported by Shanghai Shu Guang Project. |
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| 摘 要： | Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-
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Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier |
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| Zhang Yankun & Liu Chongqing Institute of Image Processing and Pattern Recognition,Shanghai Jiao long University,Shanghai P.R.China.Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier[J].Journal of Systems Engineering and Electronics,2003,14(3). |
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| Authors: | Zhang Yankun & Liu Chongqing Institute of Image Processing and Pattern Recognition Shanghai Jiao long University Shanghai PRChina |
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| Affiliation: | Institute of Image Processing and Pattern Recognition, Shanghai Jiao tong University, Shanghai 200030 P. R. China |
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| Abstract: | Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an algorithm by combining SVM classifier with NNC to improve the correct recognition rate. We conduct the experiment on the Cambridge ORL face database. The result shows that our approach outperforms the standard eigenface approach and some other approaches. |
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| Keywords: | Face recognition Support vector machine Nearest neighbor classifier Principal component analysis |
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