  Deep convolutional neural networks (CNNs) have greatly improved the Face
Recognition (FR) performance in recent years. Almost all CNNs in FR are trained
on the carefully labeled datasets containing plenty of identities. However,
such high-quality datasets are very expensive to collect, which restricts many
researchers to achieve state-of-the-art performance. In this paper, we propose
a framework, called SeqFace, for learning discriminative face features. Besides
a traditional identity training dataset, the designed SeqFace can train CNNs by
using an additional dataset which includes a large number of face sequences
collected from videos. Moreover, the label smoothing regularization (LSR) and a
new proposed discriminative sequence agent (DSA) loss are employed to enhance
discrimination power of deep face features via making full use of the sequence
data. Our method achieves excellent performance on Labeled Faces in the Wild
(LFW), YouTube Faces (YTF), only with a single ResNet. The code and models are
publicly available on-line (https://github.com/huangyangyu/SeqFace).
