Cost-sensitive face recognition

被引:0
作者
Zhang, Yin [1 ]
Zhou, Zhi-Hua [1 ]
机构
[1] Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing 210093, Peoples R China
来源
2008 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-12 | 2008年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Traditional face recognition systems attempt to achieve a high recognition accuracy, which implicitly assumes that the losses of all misclassifications are the same. However, in many real-world tasks this assumption is not always reasonable. For example, it will be troublesome if a face-recognition-based door-locker misclassifies a family member as a stranger such that s/he were not allowed to enter the house; but it will be a much more serious disaster if a stranger were misclassified as a family member and allowed to enter the house. In this paper, we propose a framework which formulates the problem as a multi-class cost-sensitive learning task, and propose a theoretically sound method based on Bayes decision theory to solve this problem. Experimental results demonstrate the effectiveness and efficiency of the proposed method.
引用
收藏
页码:3674 / 3681
页数:8
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