Multi-Branch with Attention Network for Hand-Based Person Recognition

Baisa, Nathanael L. and Williams, Bryan and Rahmani, Hossein and Angelov, Plamen and Black, Sue (2022) Multi-Branch with Attention Network for Hand-Based Person Recognition. In: 2022 26th International Conference on Pattern Recognition, ICPR 2022 :. Proceedings - International Conference on Pattern Recognition . IEEE, CAN, pp. 727-732. ISBN 9781665490627

Abstract

In this paper, we propose a novel hand-based person recognition method for the purpose of criminal investigations since the hand image is often the only available information in cases of serious crime such as sexual abuse. Our proposed method, Multi-Branch with Attention Network (MBA-Net), incorporates both channel and spatial attention modules in branches in addition to a global (without attention) branch to capture global structural information for discriminative feature learning. The attention modules focus on the relevant features of the hand image while suppressing the irrelevant backgrounds. In order to overcome the weakness of the attention mechanisms, equivariant to pixel shuffling, we integrate relative positional encodings into the spatial attention module to capture the spatial positions of pixels. Extensive evaluations on two large multi-ethnic and publicly available hand datasets demonstrate that our proposed method achieves state-of-the-art performance, surpassing the existing hand-based identification methods. The source code is available at https://github.com/nathanlem1/MBA-Net.