  With the recent success of computer vision and deep learning, remarkable
progress has been achieved on automatic personal recognition using vein
biometrics. However, collecting large-scale real-world training data for palm
vein recognition has turned out to be challenging, mainly due to the noise and
irregular variations included at the time of acquisition. Meanwhile, existing
palm vein recognition datasets are usually collected under near-infrared light,
lacking detailed annotations on attributes (e.g., pose), so the influences of
different attributes on vein recognition have been poorly investigated.
Therefore, this paper examines the suitability of synthetic vein images
generated to compensate for the urgent lack of publicly available large-scale
datasets. Firstly, we present an overview of recent research progress on palm
vein recognition, from the basic background knowledge to vein anatomical
structure, data acquisition, public database, and quality assessment
procedures. Then, we focus on the state-of-the-art methods that have allowed
the generation of vascular structures for biometric purposes and the modeling
of biological networks with their respective application domains. In addition,
we review the existing research on the generation of style transfer and
biological nature-based synthetic palm vein image algorithms. Afterward, we
formalize a general flowchart for the creation of a synthetic database
comparing real palm vein images and generated synthetic samples to obtain some
understanding into the development of the realistic vein imaging system.
Ultimately, we conclude by discussing the challenges, insights, and future
perspectives in generating synthetic palm vein images for further works.
