IEEE Trans Image Process. 2016 Jul;25(7):3261-3272. doi: 10.1109/TIP.2016.2545249. Epub 2016 Mar 22.
Sparse representation has shown its merits in solving some classification problems and delivered some impressive results in face recognition. However, the unsupervised optimization of the sparse representation may result in undesired classification outcome if the variations of the data population are not well represented by the training samples. In this paper, a method of class-wise sparse representation (CSR) is proposed to tackle the problems of the conventional sample-wise sparse representation and applied to face recognition. It seeks an optimum representation of the query image by minimizing the class-wise sparsity of the training data. To tackle the problem of the uncontrolled training data, this paper further proposes a collaborative patch (CP) framework, together with the proposed CSR, named CSR-CP. Different from the conventional patch-based methods that optimize each patch representation separately, the CSR-CP approach optimizes all patches together to seek a CP groupwise sparse representation by putting all patches of an image into a group. It alleviates the problem of losing discriminative information in the training data caused by the partition of the image into patches. Extensive experiments on several benchmark face databases demonstrate that the proposed CSR-CP significantly outperforms the sparse representation-related holistic and patch-based approaches.
稀疏表示在解决某些分类问题方面表现出了其优势，并在人脸识别方面取得了一些令人印象深刻的成果。然而，如果数据群体的变化没有被训练样本很好地表示出来，那么无监督的稀疏表示的优化可能会导致不理想的分类结果。在本文中，提出了一种基于类别的稀疏表示 (CSR) 方法来解决传统的基于样本的稀疏表示的问题，并将其应用于人脸识别。它通过最小化训练数据的类间稀疏性来寻求查询图像的最佳表示。为了解决不受控制的训练数据的问题，本文进一步提出了一种协同补丁 (CP) 框架，与所提出的 CSR 一起，命名为 CSR-CP。与传统的基于补丁的方法不同，传统的基于补丁的方法分别优化每个补丁的表示，CSR-CP 方法通过将图像的所有补丁放入一个组中，共同优化所有补丁，以寻求 CP 组间稀疏表示。它缓解了由于图像划分为补丁而导致训练数据中丢失判别信息的问题。在几个基准人脸数据库上的广泛实验表明，所提出的 CSR-CP 显著优于基于稀疏表示的整体和基于补丁的方法。
