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.

通过应用软生物特征属性和自我注意力蒸馏方法，我们提出了一种新颖的多分支神经网络，用于提高低质量图像下面部识别的性能，实验证明我们的方法优于最先进的研究成果。

CATFace: 基于跨属性引导变压器和自注意力蒸馏的低质量人脸识别

CATFace: Cross-Attribute-Guided Transformer with Self-Attention  Distillation for Low-Quality 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.

我们提出了一种新的多分支神经网络，同时作为辅助模态进行软生物测量预测并作为主任务进行人脸识别。我们的网络 AAFace 利用软生物测量属性来增强人脸识别表征的区分能力，并通过属性感知注意整合模块（AAI）对人脸识别与软生物测量特征图进行加权整合。实验结果验证了我们提出的网络在软生物测量预测和人脸识别方法方面的卓越性。