Innovative Deep Learning Architecture for Enhanced Altered Fingerprint Recognition

• URL: http://arxiv.org/abs/2509.20537v1
• Date: Wed, 24 Sep 2025 20:12:37 GMT
• Title: Innovative Deep Learning Architecture for Enhanced Altered Fingerprint Recognition
• Authors: Dana A Abdullah, Dana Rasul Hamad, Bishar Rasheed Ibrahim, Sirwan Abdulwahid Aula, Aso Khaleel Ameen, Sabat Salih Hamadamin,
• Abstract summary: We present DeepAFRNet, a deep learning recognition model that matches and recognizes distorted fingerprint samples.<n>The approach uses a VGG16 backbone to extract high-dimensional features and cosine similarity to compare embeddings.<n>With strict thresholds, DeepAFRNet achieves accuracies of 96.7 percent, 98.76 percent, and 99.54 percent for the three levels.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Altered fingerprint recognition (AFR) is challenging for biometric verification in applications such as border control, forensics, and fiscal admission. Adversaries can deliberately modify ridge patterns to evade detection, so robust recognition of altered prints is essential. We present DeepAFRNet, a deep learning recognition model that matches and recognizes distorted fingerprint samples. The approach uses a VGG16 backbone to extract high-dimensional features and cosine similarity to compare embeddings. We evaluate on the SOCOFing Real-Altered subset with three difficulty levels (Easy, Medium, Hard). With strict thresholds, DeepAFRNet achieves accuracies of 96.7 percent, 98.76 percent, and 99.54 percent for the three levels. A threshold-sensitivity study shows that relaxing the threshold from 0.92 to 0.72 sharply degrades accuracy to 7.86 percent, 27.05 percent, and 29.51 percent, underscoring the importance of threshold selection in biometric systems. By using real altered samples and reporting per-level metrics, DeepAFRNet addresses limitations of prior work based on synthetic alterations or limited verification protocols, and indicates readiness for real-world deployments where both security and recognition resilience are critical.

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