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, age, 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 ed, 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 real-world FR benchmarks varying in quality. Experimental results demonstrate the superiority of our FR method compared to state-of-the-art FR studies.
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AAFACE: Attribute-Aware Attentional Network for Face Recognition
In this paper, we present a new multi-branch neural network that simultaneously performs soft biometric (SB) prediction as an auxiliary modality and face recognition (FR) as the main task. Our proposed network named AAFace utilizes SB attributes to enhance the discriminative ability of FR representation. To achieve this goal, we propose an attribute-aware attentional integration (AAI) module to perform weighted integration of FR with SB feature maps. Our proposed AAI module is not only fully context-aware but also capable of learning complex relationships between input features by means of the sequential multi-scale channel and spatial sub-modules. Experimental results verify the superiority of our proposed network compared with the state-of-the-art (SoTA) SB prediction and FR methods.
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- Award ID(s):
- 1650474
- PAR ID:
- 10496384
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Int. Conf. on Image Processing (ICIP’23)
- ISBN:
- 978-1-7281-9835-4
- Page Range / eLocation ID:
- 1940 to 1944
- Format(s):
- Medium: X
- Location:
- Kuala Lumpur, Malaysia
- Sponsoring Org:
- National Science Foundation
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