Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes
Abstract
Fingerprint recognition is often a game-changing step in establishing evidence against criminals. However, we are increasingly finding that criminals deliberately alter their fingerprints in a variety of ways to make it difficult for technicians and automatic sensors to recognize their fingerprints, making it tedious for investigators to establish strong evidence against them in a forensic procedure. In this sense, deep learning comes out as a prime candidate to assist in the recognition of damaged fingerprints. In particular, convolution algorithms. In this paper, we focus on the recognition of damaged fingerprints by Convolutional Long Short-Term Memory networks. We present the architecture of our model and demonstrate its performance which exceeds 95% accuracy, 99% precision, and approaches 95% recall and 99% AUC.
Cite
@article{arxiv.2012.15041,
title = {Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes},
author = {Jaouhar Fattahi and Mohamed Mejri},
journal= {arXiv preprint arXiv:2012.15041},
year = {2021}
}
Comments
This paper was accepted, on December 5, 2020, for publication and oral presentation at the 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP 2021) to be held in Zhuhai, China during January 8-10, 2021 and hosted by Beijing Normal University (Zhuhai)