基于KL距离的KPCA人脸识别算法

KPCA face recognition based on KL divergence

针对核主成分分析(KPCA)人脸识别算法中对全局特征变化敏感和忽略局部特征的问题,研究了一种基于KL距离的KPCA人脸识别算法。利用KL距离定义了类间距离和类内差异,设定了一个非线性优化函数来最大化类间距离,同时最小化类内差异,使提取的特征更为紧凑可分,并将其应用于KPCA算法中,利用ORL人脸图像库对算法的性能进行了测试。实验结果表明,该算法相对于传统KPCA算法具有更好的识别效果和稳定性。

In order to solve the problem that Kernel Principal Component Analysis face recognition algorithm is sensitive to the change of the overall features, a KPCA face recognition algorithm based on KL divergence is proposed. Using KL distance between-classes distance and within-class dissimilarities are defined. A nonlinear optimization function is established, through that between-classes distance can be maximized, while within-class dissimilarities are minimized, and extracted features are more compact and separable, which is applied to KPCA algorithm. Finally via using ORL face image database the performance of the algorithm is tested. The experimental results show that the algorithm compared with traditional KPCA algorithm has better recognition effect and stability.