A novel autocorrelation and discrete cosine transform (AC/DCT) method for ECG-based biometric recognition achieved 100% subject recognition without requiring fiducial point detection.
An autocorrelation and discrete cosine transform-based method for ECG biometric recognition achieves comparable accuracy to traditional fiducial-detection methods without requiring exact wave boundary localization.
Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Existing solutions for biometric recognition from electrocardiogram (ECG) signals are based on temporal and amplitude distances between detected fiducial points. Such methods rely heavily on the accuracy of fiducial detection, which is still an open problem due to the difficulty in exact localization of wave boundaries. This paper presents a systematic analysis for human identification from ECG data. A fiducial-detection-based framework that incorporates analytic and appearance attributes is first introduced. The appearance-based approach needs detection of one fiducial point only. Further, to completely relax the detection of fiducial points, a new approach based on autocorrelation (AC) in conjunction with discrete cosine transform (DCT) is proposed. Experimentation demonstrates that the AC/DCT method produces comparable recognition accuracy with the fiducial-detection-based approach.
Wang et al. (Wed,) conducted a other in Healthy (Biometric recognition) (n=26). Autocorrelation and Discrete Cosine Transform (AC/DCT) method vs. Fiducial-detection-based methods was evaluated on Subject and window/heartbeat recognition rate. A novel autocorrelation and discrete cosine transform (AC/DCT) method for ECG-based biometric recognition achieved 100% subject recognition without requiring fiducial point detection.