ECG and fingerprint bimodal authentication

Security research on smart cities mostly focuses on the communication of “things” and rarely focuses on the interaction between “things” and people. Security breaches at this lower level will render higher-level security measures ineffective. In this paper, we propose to use a multimodal biometric approach (ECG and Fingerprint) to secure the interaction between “things” and people. ECG biometrics is a relatively new technique and its performance is still inferior to that of fingerprint biometrics. However, as opposed to ECG, fingerprint users touch objects and inadvertently leave behind their invisible fingerprints marks. Hackers can lift these invisible marks and gain illicit access to the devices of their victims. Moreover, ECG based authentication requires users to be alive, a quality that can prove important for smart cities. The latter is not true for fingerprint authentication. In this paper, we combine ECG and Fingerprint features in a bimodal biometric system. Our objective is to enhance the advantages of both biometric methods while minimizing their weaknesses. For the ECG method, we use an SVM classifier and for the Fingerprint technique we employ the minutiae extractor and matcher from NBIS. We fuse the results of the ECG and Fingerprint authentication at the decision level to distinguish between genuine users and impostors. The literature on ECG and Fingerprint bimodal biometrics is very limited, however, the obtained results show that this work presents an improvement in terms of EER (Equal Error Rate) compared to existing work.