Face recognition for a single sample per person is challenging due to the lack of sufficient sample information. However, using generic training set to learn an auxiliary dictionary is an effective way to alleviate this problem. Considering generic training sample of diversity, we proposed an algorithm of auxiliary dictionary of diversity learning (ADDL). We first produced virtual face images by mirror images, square block occlusion and grey transform, and then learned an auxiliary dictionary of diversity using a designed objective function. Considering patch-based method can reduce the influence of variations, we seek extended sparse representation with l2-minimization for each probe patch. Experimental results in the CMUPIE, Extended Yale B and LFW datasets demonstrate that ADDL performs better than other related algorithms.

中文翻译：

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人脸识别多样性学习辅助词典

由于缺乏足够的样本信息，每个人的单个样本的人脸识别具有挑战性。然而，使用通用训练集来学习辅助字典是缓解这个问题的有效方法。考虑到通用的多样性训练样本，我们提出了一种多样性学习辅助字典（ADDL）算法。我们首先通过镜像、方块遮挡和灰度变换生成虚拟人脸图像，然后使用设计的目标函数学习多样性辅助字典。考虑到基于补丁的方法可以减少变化的影响，我们寻求扩展稀疏表示2- 最小化每个探针贴片。CMUPIE、Extended Yale B 和 LFW 数据集的实验结果表明，ADDL 的性能优于其他相关算法。