基于稀疏表征多分类器融合的遮挡人脸识别

为了同时利用人脸局部信息, 提出一种基于稀疏表征多分类器融合的遮挡人脸识别方法。先对人脸进行多分辨率分块, 求取并根据各子块稀疏表征分类器的识别率确定其权重, 计算其后验概率估值, 最终利用加权融合准则进行多分类器融合识别。在AR和YaleA库的实验结果表明, 该算法结果比稀疏表征遮挡人脸识别的效果更好, 鲁棒性更高。

Sparse representation based multi-classifier fusionapproach for occluded face recognition

This paper proposed a sparse representation based multi-classification fusion (WMSRC) algorithm on the basis of analyzing different discrimination ability of the various classifiers. In WMSRC, it firstly divided the face image into partitions by multi-resolution blocking. Then for each block, it obtained SRC based classifiers. Finally, performed the multiple classifiers fusion by weighted fusion criterion, which based on the weight and posterior probability of the various classifiers. Experiment results on the AR and Yale database prove that the algorithm performance is efficient.