Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning

TL;DR

This paper introduces GPA-Net, a deep learning model that combines global and part-aware features for hand-based person identification, especially useful in uncontrolled and challenging scenarios like crime investigations.

Contribution

It proposes a novel deep network that learns both global and local features without external cues, improving identification accuracy in hand images.

Findings

Significantly outperforms existing methods on large hand datasets.

Effective in uncontrolled conditions with diverse ethnicities.

Utilizes soft partitioning for local feature extraction without external cues.

Abstract

In cases of serious crime, including sexual abuse, often the only available information with demonstrated potential for identification is images of the hands. Since this evidence is captured in uncontrolled situations, it is difficult to analyse. As global approaches to feature comparison are limited in this case, it is important to extend to consider local information. In this work, we propose hand-based person identification by learning both global and local deep feature representations. Our proposed method, Global and Part-Aware Network (GPA-Net), creates global and local branches on the conv-layer for learning robust discriminative global and part-level features. For learning the local (part-level) features, we perform uniform partitioning on the conv-layer in both horizontal and vertical directions. We retrieve the parts by conducting a soft partition without explicitly partitioning the images or requiring external cues such as pose estimation. We make extensive evaluations on two large multi-ethnic and publicly available hand datasets, demonstrating that our proposed method significantly outperforms competing approaches.