Robust ECG Biometrics by Fusing Temporal and Cepstral Information

The use of vital signs as a biometric is a potentially viable approach in a variety of application scenarios such as security and personalized health care. In this paper, a novel robust Electrocardiogram (ECG) biometric algorithm based on both temporal and cepstral information is proposed. First, in the time domain, after pre-processing and normalization, each heartbeat of the ECG signal is modeled by Hermite polynomial expansion (HPE) and support vector machine (SVM). Second, in the homomorphic domain, cepstral features are extracted from the ECG signals and modeled by Gaussian mixture modeling (GMM). In the GMM framework, heteroscedastic linear discriminant analysis and GMM super vector kernel is used to perform feature dimension reduction and discriminative modeling, respectively. Finally, fusion of both temporal and cepstral system outcomes at the score level is used to improve the overall performance. Experiment results show that the proposed hybrid approach achieves 98.3% accuracy and 0.5% equal error rate on the MIT-BIH Normal Sinus Rhythm Database.