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| Face Feature Selection and Recognition Based on Different Types of Margin |
| Li Wei-hong; Chen Wei-min; Yang Li-ping; Gong Wei-guo |
| Key Lab of Optoelectronic Technology and Systems of Education Ministry of China, Chongqing University, Chongqing 400044, China |
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Abstract Margin plays an important role in research of machine learning. Margin-based feature selection methods choose the weights of features from the view of classification. This paper analyzes different types of margin and proposed methods to improve the Sequential Backward Selection (SBS) method respectively using sample-margin and hypothesis-margin as feature selection criterion. A SVM polynomial classifier, which has optimal hyper-parameters, is then designed for face recognition. Experiments are conducted on FERET face database. Recognition accuracies between the proposed methods and relief feature selection method are compared. Experiments are also conducted by respectively using SVM and Nearest Neighbor (NN) classifier. Experimental results indicate that the proposed feature selection and recognition methods are efficient for face recognition.
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Received: 05 December 2005
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2007, Vol. 29 
