CATFace: Cross-Attribute-Guided Transformer with Self-Attention Distillation for Low-Quality Face Recognition
arXiv:2401.03037 · doi:10.1109/TBIOM.2023.3349218
Abstract
Although face recognition (FR) has achieved great success in recent years, it is still challenging to accurately recognize faces in low-quality images due to the obscured facial details. Nevertheless, it is often feasible to make predictions about specific soft biometric (SB) attributes, such as gender, and baldness even in dealing with low-quality images. In this paper, we propose a novel multi-branch neural network that leverages SB attribute information to boost the performance of FR. To this end, we propose a cross-attribute-guided transformer fusion (CATF) module that effectively captures the long-range dependencies and relationships between FR and SB feature representations. The synergy created by the reciprocal flow of information in the dual cross-attention operations of the proposed CATF module enhances the performance of FR. Furthermore, we introduce a novel self-attention distillation framework that effectively highlights crucial facial regions, such as landmarks by aligning low-quality images with those of their high-quality counterparts in the feature space. The proposed self-attention distillation regularizes our network to learn a unified quality-invariant feature representation in unconstrained environments. We conduct extensive experiments on various FR benchmarks varying in quality. Experimental results demonstrate the superiority of our FR method compared to state-of-the-art FR studies.
arXiv commentAccepted in IEEE Transactions on Biometrics, Behavior, and Identity Science (T-BIOM), 2024
References in corpus (12)
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Distilling the Knowledge in a Neural Network
- Gaussian Error Linear Units (GELUs)
- Learning Face Representation from Scratch
- Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
- NormFace: L2 Hypersphere Embedding for Face Verification
- Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments
- Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation
- MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
- Joint Face Image Restoration and Frontalization for Recognition
- Face Transformer for Recognition
- Enhancing Low-resolution Face Recognition with Feature Similarity Knowledge Distillation