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

TL;DR

This paper introduces MBA-Net, a novel multi-branch attention network that enhances hand-based person recognition by focusing on discriminative features and spatial positions, achieving state-of-the-art results in criminal investigation contexts.

Contribution

The paper presents a new multi-branch attention network with integrated positional encodings for improved hand recognition accuracy, addressing limitations of previous methods.

Findings

Achieves state-of-the-art performance on large hand datasets.

Effectively captures discriminative hand features while suppressing background.

Outperforms existing hand recognition methods.

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.