基于判别低秩矩阵恢复和协同表示的遮挡人脸识别

针对训练样本和测试样本均受到严重的噪声污染的人脸识别问题，传统的子空间学习方法和经典的基于稀疏表示的分类(SRC)方法的识别性能都将急剧下降。另外，基于稀疏表示的方法也存在算法复杂度较高的问题。为了在一定程度上缓解上述问题，提出一种基于判别低秩矩阵恢复和协同表示的遮挡人脸识别方法。首先，低秩矩阵恢复可以有效地从被污损的训练样本中恢复出干净的、具备低秩结构的训练样本，而结构非相关性约束的引入可以有效提高恢复数据的鉴别能力。然后，通过学习原始污损数据与恢复出的低秩数据之间的低秩投影矩阵，将受污损的测试样本投影到相应的低维子空间，以修正污损测试样本。最后，利用协同表示的分类方法(CRC)对修正后的测试样本进行分类，获取最终的识别结果。在Extended Yale B和AR数据库上的实验结果表明，本文方法对遮挡人脸识别具有更好的识别性能。

Occlusion face recognition based on discriminant low-rank matrix recovery and collaborative representation

In the field of face recognition, when the training samples and test samples are subject to severe noise pollution, the performance of the traditional subspace learning and the classical sparse re-presentation (SRC) will drop sharply. In addition, the method based on sparse representation also faces the problem of computational complexity. In order to alleviate those problems, an occlusion face recognition method based on discriminating low-rank matrix recovery and collaborative representation is proposed. Firstly, the low-rank matrix recovery can recover the clean training samples with low-rank structure from the contaminated training samples and the structural non-correlation constraints can improve the discriminating ability of the recovered data effectively. Secondly, by learning the low-rank projection matrix between the original contaminated data and the recovered low-rank data, the contaminated test samples are projected into the corresponding low dimensional subspace to perform its correction. Finally, the modified test samples are classified by the collaborative representation classification method (CRC) to obtain the final recognition result. Experimental results on Extended Yale B and AR databases show that the proposed method has better recognition performance in occlusion face recognition.