SeqFace: Learning discriminative features by using face sequences

Abstract 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, a framework, called SeqFace, for learning discriminative face features is proposed. 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 the discrimination power of deep face features via making full use of the sequence data. Only with a single ResNet model, the method achieves very competitive performance on several face recognition benchmarks, including LFW, YTF, CFP, AgeDB, and MegaFace. The code and model are publicly available at the website https://github.com/huangyangyu/SeqFace.