Pose variation has been one of the challenges of face recognition. To solve this challenge, the authors propose a classification algorithm using supervised subspace learning and non-local representation (SSLNR). In SSLNR, they first propose a supervised subspace learning algorithm (SSLA). SSLA includes three different terms. The first term is the difference term, which can reduce the intra-class differences. The second term is the block-diagonal regularisation term, which promotes the samples to be represented by intra-class samples. The last one is the noise robust term. Then, the original samples are mapped to the learned subspace by using SSLA. Thus, the intra-class differences of the samples mapped to the learned subspace are reduced. Finally, those mapped samples are classified by proposed non-local constraint-based extended sparse representation classifier. SSLNR is extensively evaluated using four databases, namely Georgia Tech, Label faces in the wild, FEI and CVL. Experimental results show that SSLNR achieves better performance than some state-of-the-art algorithms, such as DARG and RRNN.

中文翻译：

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使用监督子空间学习和非局部表示的姿态变化识别算法

姿势变化一直是面部识别的挑战之一。为了解决这一挑战，作者提出了一种使用监督子空间学习和非本地表示（SSLNR）的分类算法。在SSLNR中，他们首先提出了一种监督子空间学习算法（SSLA）。SSLA包括三个不同的术语。第一项是差异项，可以减少类内差异。第二项是块对角正则化项，它促使样本由类内样本表示。最后一个是噪声鲁棒性术语。然后，使用SSLA将原始样本映射到学习的子空间。因此，减小了映射到学习的子空间的样本的类内差异。最后，那些映射的样本由建议的基于非局部约束的扩展稀疏表示分类器分类。SSLNR已使用四个数据库进行了广泛评估，这些数据库分别是Georgia Tech，Label in wild，FEI和CVL。实验结果表明，与某些最新算法（例如DARG和RRNN）相比，SSLNR具有更好的性能。