A New Face Recognition Approach to Boosting the Worst-Case Performance

被引:0
|
作者
Chen, Fang [1 ]
An, Senjian [1 ]
Liu, Wanquan [1 ]
机构
[1] Curtin Univ Technol, Dept Comp, Bentley, WA 6102, Australia
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we aim to develop a new classifier for increasing the worst case performance for individual person. Technically, we adopt the idea from LDA and improve the worst recognition performance for individuals. This is achieved via introducing different weighting coefficients in LDA optimization process for obtaining the projection matrix. By increasing the weighting coefficients associated with the smallest between-class distance, the pair of classes with the nearest distance can exert more powerful influence on the optimization process in derivation of the projection matrix. The algorithm is tested on the Extended YaleB dataset and the ORL dataset.
引用
收藏
页码:927 / 930
页数:4
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