Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Multi-Branch with Attention Network for Hand-Based Person Recognition
AU - Baisa, Nathanael L.
AU - Williams, Bryan
AU - Rahmani, Hossein
AU - Angelov, Plamen
AU - Black, Sue
PY - 2022/11/29
Y1 - 2022/11/29
N2 - 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.
AB - 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.
U2 - 10.1109/ICPR56361.2022.9956555
DO - 10.1109/ICPR56361.2022.9956555
M3 - Conference contribution/Paper
AN - SCOPUS:85138498461
T3 - Proceedings - International Conference on Pattern Recognition
SP - 727
EP - 732
BT - 2022 26th International Conference on Pattern Recognition, ICPR 2022
PB - IEEE
T2 - 26th International Conference on Pattern Recognition, ICPR 2022
Y2 - 21 August 2022 through 25 August 2022
ER -