  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.
