Analysis of Human Electrocardiogram for Biometric Recognition
By Yongjin Wang, Foteini Agrafioti, Dimitrios Hatzinakos, K. Plataniotis. EURASIP Journal on Advances in Signal Processing, 2008
399 citations recorded by the source corpus.
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.
Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/73d41795c2e8b36ee235101153fa1a8d9e65e9f7
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- J. Rohrbaugh
Citations in the corpus
- Deep-ECG: Convolutional Neural Networks for ECG biometric recognition (2019, background): “In addition, the QRS complex is less sensible to physical and emotional variations [40].”
- ECG Biometric Recognition: A Comparative Analysis (2012, methodology): “3) LDA Classifiers: Classification based on linear discriminant analysis, a special case of generative model classifiers (GMCs), has been used by several studies in the ECG biometrics literature [6], [7], [21], [22], [25], [34], [35], [42].”