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Collaborative Representation-Based Robust Face Recognition by Discriminative Low-Rank Representation

机译:区分低秩表示的基于协同表示的鲁棒人脸识别

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摘要

We consider the problem of robust face recognition in which both the training and test samples might be corrupted because of disguise and occlusion. Performance of conventional subspace learning methods and recently proposed sparse representation based classification (SRC) might be degraded when corrupted training samples are provided. In addition, sparsity based approaches are time-consuming due to the sparsity constraint. To alleviate the aforementioned problems to some extent, in this paper, we propose a discriminative low-rank representation method for collaborative representation-based (DLRR-CR) robust face recognition. DLRR-CR not only obtains a clean dictionary, it further forces the sub-dictionaries for distinct classes to be as independent as possible by introducing a structural incoherence regularization term. Simultaneously, a low-rank projection matrix can be learned to remove the possible corruptions in the testing samples. Collaborative representation based classification (CRC) method is exploited in our proposed method which has a closed-form solution. Experimental results obtained on public face databases verify the effectiveness and robustness of our method.
机译:我们考虑到人脸识别能力强的问题,其中训练和测试样本都可能由于伪装和遮挡而损坏。提供损坏的训练样本时,常规子空间学习方法和最近提出的基于稀疏表示的分类(SRC)的性能可能会降低。另外,由于稀疏性的限制,基于稀疏性的方法非常耗时。为了在某​​种程度上缓解上述问题,我们提出了一种基于协作表示的鲁棒性人脸识别的判别性低秩表示方法。 DLRR-CR不仅获得了简洁的字典,而且还通过引入结构不一致性正则项来强制不同类的子词典尽可能独立。同时,可以学习低秩投影矩阵,以消除测试样本中可能出现的损坏。在我们提出的具有封闭形式解决方案的方法中,利用了基于协作表示的分类(CRC)方法。在公众面部数据库上获得的实验结果证明了我们方法的有效性和鲁棒性。

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