基于奇异值分解的分类器及在人脸识别中的应用

A Classifier Based on Singular Value Decomposition and Its Application to Face Recognition

提出了利用一个人脸样本的奇异值分解构造其所在类别分类器的方法,解决了人脸识别中训练样本少的问题,并给出了利用错归样本更新分类器的方法.实验表明每类仅有一个训练样本就可以得到满意的识别率,并且仅利用一个错归样本更新分类器还可以使识别率进一步提高.

A new kind of classifier based on singular value decomposition of face images which solves the small sample size problem in face recognition tasks is presented in this paper. The method of renewing classifier using the incorrectly classified face image to improve the performance of classifiers is also given. Experimental results show that high recognition rate can be obtained by using only one sample per person and that the renewal of classifiers using only one incorrectly classified sample can further improve the performance of classifiers.