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

Date

2022-04-20

Advisors

Journal Title

Journal ISSN

ISSN

2154-512X
2154-5111

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

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.

Description

Keywords

Person identification, Hand recognition, Deep representation learning, Global features, Part-level features

Citation

Baisa, N.L., Williams, B., Rahmani, H., Angelov, P. and Black, S. (2022) Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning. In: 2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA)

Rights

Research Institute

Institute of Artificial Intelligence (IAI)