Face Transformer for Recognition

Abstract

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.

References in corpus (6)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• A Survey on Visual Transformer
• Learning Face Representation from Scratch
• Transformer in Transformer
• Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments

Cited by in corpus (8)

• SwinFace: A Multi-task Transformer for Face Recognition, Expression Recognition, Age Estimation and Attribute Estimation
• Cross-Quality LFW: A Database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments
• Explainable Model-Agnostic Similarity and Confidence in Face Verification
• Octuplet Loss: Make Face Recognition Robust to Image Resolution
• Learning Fair Face Representation With Progressive Cross Transformer
• CATFace: Cross-Attribute-Guided Transformer with Self-Attention Distillation for Low-Quality Face Recognition
• T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers
• DBIA: Data-free Backdoor Injection Attack against Transformer Networks