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.
@article{arxiv.2308.07243,
title = {AAFACE: Attribute-aware Attentional Network for Face Recognition},
author = {Niloufar Alipour Talemi and Hossein Kashiani and Sahar Rahimi Malakshan and Mohammad Saeed Ebrahimi Saadabadi and Nima Najafzadeh and Mohammad Akyash and Nasser M. Nasrabadi},
journal= {arXiv preprint arXiv:2308.07243},
year = {2023}
}
Comments
Accepted to $30^{th}$ IEEE International Conference on Image Processing (ICIP 2023) as an oral presentation