  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.
