  Deep Convolutional Neural Networks (CNNs) are widespread, efficient tools of
visual recognition. In this paper, we present a comparative study of three
popular pre-trained CNN models: AlexNet, VGG-16 and VGG-19. We address the
problem of palmprint identification in low-quality imagery and apply Support
Vector Machines (SVMs) with all of the compared models. For the comparison, we
use the MOHI palmprint image database whose images are characterized by low
contrast, shadows, and varying illumination, scale, translation and rotation.
Another, high-quality database called COEP is also considered to study the
recognition gap between high-quality and low-quality imagery. Our experiments
show that the deeper pre-trained CNN models, e.g., VGG-16 and VGG-19, tend to
extract highly distinguishable features that recognize low-quality palmprints
more efficiently than the less deep networks such as AlexNet. Furthermore, our
experiments on the two databases using various models demonstrate that the
features extracted from lower-level fully connected layers provide higher
recognition rates than higher-layer features. Our results indicate that
different pre-trained models can be efficiently used in touchless
identification systems with low-quality palmprint images.
