  Recently there has been a growing interest in Transformer not only in NLP but
also in computer vision. We wonder if transformer can be used in face
recognition and whether it is better than CNNs. Therefore, we investigate the
performance of Transformer models in face recognition. Considering the original
Transformer may neglect the inter-patch information, we modify the patch
generation process and make the tokens with sliding patches which overlaps with
each others. The models are trained on CASIA-WebFace and MS-Celeb-1M databases,
and evaluated on several mainstream benchmarks, including LFW, SLLFW, CALFW,
CPLFW, TALFW, CFP-FP, AGEDB and IJB-C databases. We demonstrate that Face
Transformer models trained on a large-scale database, MS-Celeb-1M, achieve
comparable performance as CNN with similar number of parameters and MACs. To
facilitate further researches, Face Transformer models and codes are available
at https://github.com/zhongyy/Face-Transformer.
