  With the growing demand for hand hygiene and convenience of use, palmprint
recognition with touchless manner made a great development recently, providing
an effective solution for person identification. Despite many efforts that have
been devoted to this area, it is still uncertain about the discriminative
ability of the contactless palmprint, especially for large-scale datasets. To
tackle the problem, in this paper, we build a large-scale touchless palmprint
dataset containing 2334 palms from 1167 individuals. To our best knowledge, it
is the largest contactless palmprint image benchmark ever collected with regard
to the number of individuals and palms. Besides, we propose a novel deep
learning framework for touchless palmprint recognition named 3DCPN (3D
Convolution Palmprint recognition Network) which leverages 3D convolution to
dynamically integrate multiple Gabor features. In 3DCPN, a novel variant of
Gabor filter is embedded into the first layer for enhancement of curve feature
extraction. With a well-designed ensemble scheme,low-level 3D features are then
convolved to extract high-level features. Finally on the top, we set a
region-based loss function to strengthen the discriminative ability of both
global and local descriptors. To demonstrate the superiority of our method,
extensive experiments are conducted on our dataset and other popular databases
TongJi and IITD, where the results show the proposed 3DCPN achieves
state-of-the-art or comparable performances.
