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

arXiv: https://arxiv.org/abs/2012.15041 · HTML: https://mlsanity.com/paper/2012.15041/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Re-Evaluation of Biometric Modality Properties in the Modern Era](https://mlsanity.com/paper/2508.13874/) (2025)

## Abstract (arXiv)

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.