Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes
This work aims to improve the recognition of damaged fingerprints, a critical problem for forensic investigators in establishing evidence against criminals.
This paper addresses the challenge of recognizing deliberately altered fingerprints for forensic purposes. The authors propose a Convolutional Long Short-Term Memory network model that achieves over 95% accuracy, 99% precision, approximately 95% recall, and 99% AUC in recognizing damaged fingerprints.
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.