Due to the absence of training samples and intraclass variation, the extraction of discriminative facial features and construction of powerful classifiers have bottlenecks in improving the performance of facial recognition (FR) with a single sample per data subject (SSPDS). In this paper, we propose to learn regional adaptive convolution features that are locally and globally discriminative to facial identity and robust to facial variation. Then, a novel class-level joint representation framework is presented to exploit the distinctiveness and class-level commonality of different facial features. In the proposed class-level joint representation with regional adaptive convolution features (CJR-RACF), both discriminative facial features that are robust to facial variations and powerful representations for classification with generic facial variations have been fully exploited. Furthermore, the gallery discrimination is extracted by our proposed weight-embedded supervision in the training phase (denoted by CJR-RACFw), which is conducive to more specific features for FR with SSPDS. CJR-RACF and CJR-RACFw have been evaluated on several popular databases, including the large-scale CMU Multi-PIE, LFW, Megaface, and VGGFace datasets. Experimental results demonstrate the much higher robustness and effectiveness of the proposed methods compared to the state-of-the-art methods.

中文翻译：

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自适应卷积局部和全局学习，用于每个数据主题具有单个样本的面部识别的类级联合表示

由于缺少训练样本和类内差异，因此区分性面部特征的提取和功能强大的分类器的构建在每个数据对象只有一个样本（SSPDS）的情况下，提高了面部识别（FR）性能的瓶颈。在本文中，我们建议学习区域自适应卷积特征，这些特征在局部和全局上对面部识别是有区别的，对面部变化具有鲁棒性。然后，提出了一种新颖的班级联合表示框架，以利用不同面部特征的独特性和班级共性。在拟议的具有区域自适应卷积特征的类级联合表示（CJR-RACF）中，对面部变化具有鲁棒性的辨别性面部特征和具有通用面部变化的分类的有力表示都已得到充分利用。此外，在我们的训练阶段（通过CJR-RACFw表示），通过我们建议的权重嵌入式监督来提取画廊歧视，这有利于SSPDS FR的更具体功能。CJR-RACF和CJR-RACFw已在几种流行的数据库上进行了评估，包括大型CMU Multi-PIE，LFW，Megaface和VGGFace数据集。实验结果表明，与最新方法相比，该方法具有更高的鲁棒性和有效性。这有助于使用SSPDS的FR具有更特定的功能。CJR-RACF和CJR-RACFw已在几种流行的数据库上进行了评估，包括大型CMU Multi-PIE，LFW，Megaface和VGGFace数据集。实验结果表明，与最新方法相比，该方法具有更高的鲁棒性和有效性。这有助于使用SSPDS的FR具有更特定的功能。CJR-RACF和CJR-RACFw已在几种流行的数据库上进行了评估，包括大型CMU Multi-PIE，LFW，Megaface和VGGFace数据集。实验结果表明，与最新方法相比，该方法具有更高的鲁棒性和有效性。