Development of an Efficient ECG and PPG Signal Processing-based Spoof Detection System using Convolutional Neural Networks

Biometric authentication systems have a large range of applications in the real-life situations. These systems receive various types of signals from the biometric sensors and process them efficiently in order to determine their authenticity. However, these systems are often vulnerable to the biometric spoof signals that are synthetically generated. In view of this, the design of the efficient spoof detection methods for the biometric authentication systems is of paramount importance. There exist various biological signal processing-based biometric authentication systems that use the combination of electrocardiogram (ECG) and photoplethysmogram (PPG) signals for performing their operations. In this paper, we propose a novel spoof detection method for the ECG and PPG signal processing-based biometric authentication systems that is able to reliably detect the spoof signals from the authentic ones. Specifically, the proposed method utilizes two stages, which the first stage involves in obtaining the authenticity status of the ECG and PPG signals separately using the convolutional neural networks and the Bayes classifier. In the second stage of the proposed scheme, the spectro-temporal features of the ECG and PPG signals are extracted in order to further enhance the performance of the task of spoof detection. It is shown that our spoof detection system is able to provide a high performance on the benchmark dataset of the biometric applications.