AAFACE: Attribute-aware Attentional Network for Face Recognition
This work addresses face recognition accuracy for applications like security or identification, but it appears incremental as it builds on existing multi-branch and attention mechanisms.
The paper tackles the problem of improving face recognition by integrating soft biometric attributes as an auxiliary modality, resulting in a network that outperforms state-of-the-art methods in both tasks.
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.