Abstract
Although face recognition systems have achieved impressive performance in recent years, the low-resolution face recognition (LRFR) task remains challenging, especially when the LR faces are captured under non-ideal conditions, as is common in surveillance-based applications. Faces captured in such conditions are often contaminated by blur, nonuniform lighting, and nonfrontal face pose. In this paper, we analyze face recognition techniques using data captured under low-quality conditions in the wild. We provide a comprehensive analysis of experimental results for two of the most important applications in real surveillance applications, and demonstrate practical approaches to handle both cases that show promising performance. The following three contributions are made: {\em (i)} we conduct experiments to evaluate super-resolution methods for low-resolution face recognition; {\em (ii)} we study face re-identification on various public face datasets including real surveillance and low-resolution subsets of large-scale datasets, present a baseline result for several deep learning based approaches, and improve them by introducing a GAN pre-training approach and fully convolutional architecture; and {\em (iii)} we explore low-resolution face identification by employing a state-of-the-art supervised discriminative learning approach. Evaluations are conducted on challenging portions of the SCFace and UCCSface datasets.
Abstract (translated)
虽然人脸识别系统近年来取得了令人印象深刻的性能，但低分辨率人脸识别（LRFR）任务仍然具有挑战性，尤其是当在非理想条件下捕获LR人脸时，这在基于监视的应用中很常见。在这种情况下捕捉到的脸经常被模糊、不均匀的灯光和非正面的面部姿势所污染。本文利用野外低质量条件下采集的数据对人脸识别技术进行了分析。我们对实际监测应用中两个最重要的应用程序的实验结果进行了全面的分析，并论证了处理这两个案例的实用方法，这些案例显示出良好的性能。以下三个方面的贡献是：em（i）我们进行实验评估低分辨率人脸识别的超分辨率方法；em（ii）我们研究了各种公共人脸数据集上的人脸重新识别，包括真实监测和大尺度数据集的低分辨率子集，给出了几个深度的基线结果。基于学习的方法，并通过引入GAN预培训方法和完全卷积的体系结构来改进这些方法；以及em（iii）我们通过采用最先进的监督鉴别学习方法来探索低分辨率人脸识别。对SCFace和UCCSFace数据集的具有挑战性的部分进行评估。
URL
https://arxiv.org/abs/1805.11529
