Adaptive Prototype Triplet Loss for Cross-Resolution Face Recognition

Yongru Chen, Wenxian Zheng, Xiaying Bai, Qiqi Bao, Wenming Yang, Guijin Wang, Qingmin Liao

Abstract: Although face recognition has achieved great success in many areas, cross-resolution face recognition (CRFR) still remains a challenging task due to the large domain gap between low-resolution (LR) and high-resolution (HR) images. In this paper, we propose an adaptive prototype triplet loss (APTL) for CRFR. The APTL pulls the features close to their own prototypes, and pushes them away from the prototypes of other classes. Thus, the angular distances between features and prototypes from the same class are closer than those from different classes. Furthermore, to better exploit the similarity information among different identities, we adaptively adjust the margin term in the loss. Since the proposed APTL is applied simultaneously to HR and LR features, the gap between two domains can be narrowed naturally. Experiments on LFW and SCface datasets illustrate the superiority of our method.