We present an approach for unsupervised training of CNNs in order to learn
discriminative face representations. We mine supervised training data by noting
that multiple faces in the same video frame must belong to different persons
and the same face tracked across multiple frames must belong to the same
person. We obtain millions of face pairs from hundreds of videos without using
any manual supervision. Although faces extracted from videos have a lower
spatial resolution than those which are available as part of standard
supervised face datasets such as LFW and CASIA-WebFace, the former represent a
much more realistic setting, e.g. in surveillance scenarios where most of the
faces detected are very small. We train our CNNs with the relatively low
resolution faces extracted from video frames collected, and achieve a higher
verification accuracy on the benchmark LFW dataset cf. hand-crafted features
such as LBPs, and even surpasses the performance of state-of-the-art deep
networks such as VGG-Face, when they are made to work with low resolution input
images.

本文提出了一种无监督学习 CNNs 的方法，通过从视频帧中提取面孔对来获得受监督的训练数据并获得比手工制作特征和最先进的深度网络更高的低分辨率验证准确性，表明开发适用于姿势和光线变化的人脸验证模型是可行的。