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

A Study on the Impact of Face Image Quality on Face Recognition in the Wild