Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes

TL;DR

This paper proposes a deep learning approach using Convolutional Long Short-Term Memory networks to improve recognition of deliberately damaged fingerprints, achieving high accuracy and precision for forensic applications.

Contribution

It introduces a novel CNN-LSTM architecture specifically designed for damaged fingerprint recognition, demonstrating superior performance over existing methods.

Findings

Accuracy exceeds 95%

Precision reaches 99%

Recall approaches 95%

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.