An advanced authentication scheme utilizing a binarized bio-key of ECG signals and an Artificial Neural Network achieved a high authentication accuracy of 98% and minimized the equal error rate to 2%.
An advanced authentication scheme using ECG signals and an Artificial Neural Network achieved 98% accuracy and a 2% equal error rate for biometric security.
The improvements in the field of health monitoring have revolutionized our daily lifestyle by developing various applications that did not exist before. However, these applications have serious security concerns; they also can be taken good care of by utilizing the Electrocardiogram (ECG) as potential biometrics. The ECG provides robustness against forgery attacks unlike conventional methods of authentication. Therefore, it has attained the utmost attention and is utilized in several authentication solutions. In this paper, we have presented an efficient architecture for an advanced authentication scheme that utilized a binarized form (bio-key) of ECG signal along with an Artificial Neural Network (ANN) to enhance the authentication process. In order to prove the concept, we have developed the testbed and acquired ECG signals using the AD8232 ECG recording module under a controlled environment. The variable-length bio-keys are extracted using an algorithm after the feature extraction process. The extracted features along with bio-keys are utilized for template formation and also for training/testing of the ANN model to enhance the accuracy of the authentication process. The performance of authentication results depicted high authentication accuracy of 98% and minimized the equal error rate (EER) to 2%. Moreover, our scheme outperformed comparative peers’ work in terms of accuracy and EER.
Rehman et al. (Sat,) conducted a other in ECG-based authentication. Advanced authentication scheme with bio-key using Artificial Neural Network vs. Comparative peers' work was evaluated on Authentication accuracy and equal error rate (EER). An advanced authentication scheme utilizing a binarized bio-key of ECG signals and an Artificial Neural Network achieved a high authentication accuracy of 98% and minimized the equal error rate to 2%.