AAFACE: Attribute-aware Attentional Network for Face Recognition
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 28 references that are in the map. First posted on arXiv 14 Aug 2023.
Builds on
17 of 28 references.
References · 17
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5 more
- 2016An All-In-One Convolutional Neural Network for Face Analysiscited by 45
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Citing papers per year: 2023: 3.
3 papers.