Deep learning has received increasing interests in face recognition recently. Large quantities of deep learning methods have been proposed to handle various problems appeared in face recognition. Quite a lot deep methods claimed that they have gained or even surpassed human-level face verification performance in certain databases. As we know, face image quality poses a great challenge to traditional face recognition methods, e.g. model-driven methods with hand-crafted features. However, a little research focus on the impact of face image quality on deep learning methods, and even human performance. Therefore, we raise a question: Is face image quality still one of the challenges for deep learning based face recognition, especially in unconstrained condition. Based on this, we further investigate this problem on human level. In this paper, we partition face images into three different quality sets to evaluate the performance of deep learning methods on cross-quality face images in the wild, and then design a human face verification experiment on these cross-quality data. The result indicates that quality issue still needs to be studied thoroughly in deep learning, human own better capability in building the relations between different face images with large quality gaps, and saying deep learning method surpasses human-level is too optimistic.

翻译：深度学习在人脸识别领域近年备受关注，大量深度学习方法被提出以应对人脸识别中的各类问题。许多深度学习方法声称在特定数据库中已达到甚至超越人类级别的人脸验证性能。众所周知，人脸图像质量对传统人脸识别方法（例如基于手工特征的模型驱动方法）构成了巨大挑战。然而，针对人脸图像质量对深度学习方法乃至人类识别性能影响的研究却相对较少。因此，我们提出一个问题：在非约束条件下，人脸图像质量是否仍是基于深度学习的人脸识别所面临的挑战之一？基于此，我们进一步从人类层面探究该问题。本文将人脸图像划分为三种不同的质量等级，评估深度学习方法在非约束环境下处理跨质量人脸图像的性能，并设计了一项基于这些跨质量数据的人类人脸验证实验。结果表明：质量问题仍需在深度学习中进行深入研究；人类在建立具有较大质量差异的不同人脸图像间的关联方面具备更优的能力；声称深度学习方法超越人类水平的说法过于乐观。