Fingerprint Reconstruction: Approaches to Improve Fingerprint Images

Fingerprint reconstruction methods have been initially proposed to spoof the fingerprint identification systems, wherein the fingerprints are generated from the fingerprint features stored in the database for template matching/identification purpose. The reconstructed fingerprints attempt to validate in the absence of the user/person. The poor fingerprint Images with scratches on fingerprint image or latent fingerprints or overlapping fingerprints shall also be reconstructed for personality identification. In this paper we discuss the two fingerprint reconstruction methods, one which uses minutiae features for reconstruction and the other one uses deep learning methods to reconstruct the fingerprint images. The poor fingerprint image which fails to validate the identity due to various reasons like poor skin condition/large cuts on the fingers/wet fingers/poor scanning of images shall be reconstructed for increasing the matching accuracy. The requirement of performance measure parameters used for evaluation of these systems are equal error rate, false acceptance rate, false rejection rate and average matching score. The deep learning methods are more suitable for reconstructing the fingerprint images that appear damaged due to poor skin condition/large cuts on the fingers/wet fingers/poor scanning of images. In terms of matching score comparison, the deep learning methods have matching scores in between 23-94% whereas for minutiae-based techniques the matching score is between 82 and 99.99%. The other performance parameter is the equal error rate (ERR) required to meet has to be closer to 0. The matching score is computed with the assumptions of false acceptance rate (FAR) ranging from 1% to 0%.