ID-Softmax: A Softmax-like Loss for ID Face Recognition

Yan Kong, Fuzhang Wu, Feiyue Huang, Yanjun Wu

Abstract: The face recognition between photos from identification documents (ID, Citizen Card or Passport Card) and daily photos, which is named FRBID(Zhang et al., 2017), is widely used in real world scenarios. However, traditional Softmax loss of deep CNN usually lacks the power of discrimination for FRBID. To address this problem, in this paper, we first revisit recent progress of face recognition losses, and give the theoretical and experimental analysis on the reason why Softmax-like losses work badly on ID-daily face recognition. Then we propose an novel approach named ID-Softmax, which use ID face features as class ’agent’ to guide the deep CNNs to learn highly discriminative features between ID photos and daily photos. In order to promote the ID-daily face recognition, we collect a large dataset ID74K, which includes 74,187 identities with corresponding ID photos and daily photos. To test our approach, we evaluate the feature distribution and face verification performance on dataset ID