Pilot Comparative Study of Different Deep Features for Palmprint Identification in Low-Quality Images
This work addresses palmprint identification in low-quality images for touchless systems, but it is incremental as it applies existing methods to a specific domain.
The study compared pre-trained CNN models (AlexNet, VGG-16, VGG-19) for palmprint identification in low-quality images, finding that deeper models like VGG-16 and VGG-19 extracted more distinguishable features and that lower-level fully connected layers provided higher recognition rates.
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.