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

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.