Fingertip electrocardiogram and speech signal based biometric recognition system

In this research work, we presented a one-dimensional CNN-based person identification system which depends on the combination of both speech and ECG modalities to improve the overall performance compared to traditional systems. The proposed method has two approach: one is to develop combination of text-independent speech and fingertip ECG fusion system, the other one is to develop a robust rejection algorithm to prevent unauthorized access to the fusion system. In addition to the system robustness, we have developed an ECG spike and inconsistent beats removing algorithm, which detect and remove the problems caused by either portable fingertip ECG devices or movements of the patients. First approach has been tested on 30, 45, 60, 75 and 90 people which were taken from LibriSpeech Corpus database and combination of both CYBHi and our private fingertip ECG database. The 3-fold cross validation test setup has been conducted while system working time was set to 10 seconds. In the first experiment, we achieved 90.22% accuracy rate for 90 people for ECG based system. For the speech based system, 97.94% accuracy rate has achieved for 90 people. For the combination of both system, 99.92% accuracy rate has been achieved. For the second approach, 90 people for ECG and Speech database were being used as genuine class, 26 people as imposter class, and after the performance evaluation in optimum rejection thresholds, 71.08% accuracy rate for imposters rejection and 71.05% accuracy rate for genuine recognition has achieved for ECG based system. For the speech based system, imposter class were 87.82% accurately rejected while genuine classes were 86.48% accurately identified. The combination of both system has achieved 91.68% accuracy for genuine identification rate whereas 96.05% accuracy for imposter rejection.