  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.
