Deep learning for partial fingerprint inpainting and recognition

Abstract

Image completion and inpainting has been widely studied by the computer vision research community. With the recent growth and availability of computation power, we are now able to perform more complex inpainting than ever before. Techniques based on both learning and non-learning methods have been proposed for image inpainting. Some of these approaches have been used for fingerprint image enhancement. However, we lack techniques for fingerprint completion using deep learning. This is especially the case for techniques with the goal of augmenting the number of correct minutiae matchpoints for fingerprint recognition. This paper proposes new deep architectures to improve the accuracy of prints matching in live scan images. The proposed techniques have been tested using a professional software for fingerprint matching to evaluate the performance of deep learning in that aspect. The obtained results are promising and show an increase of 36.94% in minutiae match points identification.