IEEE Trans Cybern. 2018 Aug;48(8):2402-2415. doi: 10.1109/TCYB.2017.2739338. Epub 2017 Aug 29.
The issue of single sample per person (SSPP) face recognition has attracted more and more attention in recent years. Patch/local-based algorithm is one of the most popular categories to address the issue, as patch/local features are robust to face image variations. However, the global discriminative information is ignored in patch/local-based algorithm, which is crucial to recognize the nondiscriminative region of face images. To make the best of the advantage of both local information and global information, a novel two-layer local-to-global feature learning framework is proposed to address SSPP face recognition. In the first layer, the objective-oriented local features are learned by a patch-based fuzzy rough set feature selection strategy. The obtained local features are not only robust to the image variations, but also usable to preserve the discrimination ability of original patches. Global structural information is extracted from local features by a sparse autoencoder in the second layer, which reduces the negative effect of nondiscriminative regions. Besides, the proposed framework is a shallow network, which avoids the over-fitting caused by using multilayer network to address SSPP problem. The experimental results have shown that the proposed local-to-global feature learning framework can achieve superior performance than other state-of-the-art feature learning algorithms for SSPP face recognition.
近年来，单人单样本（SSPP）人脸识别问题受到了越来越多的关注。基于补丁/局部的算法是解决该问题的最受欢迎的类别之一，因为补丁/局部特征对人脸图像变化具有很强的鲁棒性。然而，基于补丁/局部的算法忽略了全局判别信息，而全局判别信息对于识别人脸图像的非判别区域至关重要。为了充分利用局部信息和全局信息的优势，提出了一种新的两层局部到全局特征学习框架来解决 SSPP 人脸识别问题。在第一层中，通过基于补丁的模糊粗糙集特征选择策略学习面向目标的局部特征。所获得的局部特征不仅对图像变化具有很强的鲁棒性，而且可以保持原始补丁的判别能力。在第二层中，通过稀疏自动编码器从局部特征中提取全局结构信息，从而减少非判别区域的负面影响。此外，所提出的框架是一个浅层网络，避免了使用多层网络来解决 SSPP 问题时的过拟合。实验结果表明，所提出的局部到全局特征学习框架在 SSPP 人脸识别方面的性能优于其他最先进的特征学习算法。
