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

• URL: http://arxiv.org/abs/2012.15041v1
• Date: Wed, 30 Dec 2020 04:51:58 GMT
• Title: Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes
• Authors: Jaouhar Fattahi and Mohamed Mejri
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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