Face recognition by support vector machines
FG（2000）
摘要
Support vector machines (SVM) have been recently proposed as a new technique for pattern recognition. SVM with a binary tree recognition strategy are used to tackle the face recognition problem. We illustrate the potential of SVM on the Cambridge ORL face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. We also present the recognition experiment on a larger face database of 1079 images of 137 individuals. We compare the SVM-based recognition with the standard eigenface approach using the nearest center classification (NCC) criterion
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关键词
high degree,larger face database,binary tree,cambridge orl face database,face recognition,pattern recognition,prin- cipal component analysis.,learning (artificial intelligence),tree data structures,recognition experiment,facial detail,optimal separating hyperplane,face recognition problem,binary tree recognition strategy,eigenface,binary tree recognition,support vector machines,nearest center classification,authentication,learning artificial intelligence,reactive power,binary trees,image recognition,support vector machine,lighting,face detection,principal component analysis
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