Using Bio-Cryptographic Key Extracted from Heartprint Signal by a Deep Neural Network for Authentication

Kocak O., Islam S., Gumus O., Yilmaz G. N.

7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, ICHORA 2025, Ankara, Turkey, 23 - 24 May 2025, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ichora65333.2025.11017184
  • City: Ankara
  • Country: Turkey
  • Keywords: biometric authentication, cryptographic key, deep machine learning, ECG signal, heartprint, neural network
  • TED University Affiliated: Yes

Abstract

A cryptographic key extracted from a biometric sample could leverage the strength of both biometrics and cryptography simultaneously. However, the random nature of this bio-cryptographic key prevents it from being used in encryption directly. To address this problem, we present a novel authentication method employing bio-cryptographic key extracted from heartprint (ECG signal) by using a Siamese Neural Network (SNN). We have used a public dataset of heartprints obtained from the fingers of 199 users. The extracted keys have demonstrated excellent cryptographic and biometric characteristics. Experimental results show that bio-cryptographic keys with entropy ∼1 achieve higher intra-similarity compared to inter-similarity. Cross-validation results show an average equal error rate (EER) of 0.02. These results underscore the robustness and feasibility of the bio-cryptographic key which can play a crucial role in preventing unauthorized access to digital content.