Joint Person Identity, Gender and Age Estimation from Hand Images using Deep Multi-Task Representation Learning

Date

2024-07-03

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

In this paper, we propose a multi-task representation learning framework to jointly estimate the identity, gender and age of individuals from their hand images for the purpose of criminal investigations since the hand images are often the only available information in cases of serious crime such as sexual abuse. We investigate different up-to-date deep learning architectures and compare their performance for joint estimation of identity, gender and age from hand images of perpetrators of serious crime. To simplify the age prediction, we create age groups for the age estimation. We make extensive evaluations and comparisons of both convolution-based and transformer-based deep learning architectures on a publicly available 11k hands dataset. Our experimental analysis shows that it is possible to efficiently estimate not only identity but also other attributes such as gender and age of suspects jointly from hand images for criminal investigations, which is crucial in assisting international police forces in the court to identify and convict abusers. The source code is available at https://github.com/nathanlem1/IGAE-Net.

Description

Keywords

Person identification, Gender estimation, Age estimation, Deep representation learning, Multi-task learning

Citation

Baisa, N.L (2024) Joint Person Identity, Gender and Age Estimation from Hand Images using Deep Multi-Task Representation Learning. 12th International Workshop on Biometrics and Forensics, 2024.

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/

Research Institute