A bag-of-words approach using sparse representations for ECG-based biometrics achieves over 99% classification accuracy without requiring waveform segmentation or fiducial points detection.
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May enable simpler ECG biometrics; leaves open multicenter validation before adoption.
The performances of the bag‐of‐words approach in biometric applications using electrocardiography (ECG) signals have been analysed according to the influence of specific design parameters. Optimal setup scenarios have been identified combining five encoding procedures, two pooling methods, and three classification strategies. The method does not require waveform segmentation nor fiducial points detection. Comparative results based on extensive experiments conducted on real ECG recordings collected on chest, finger, and hand palm are presented. Sparse representations yield best results, exceeding 99% correct classification rate for a number of 100 subjects, while additionally exhibiting robustness against modifications of the experimental setup.
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Iulian B. Ciocoiu (2017) studied this question.
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