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Article type: Research Article
Authors: Yang, Junfeng | Huang, Yuwen | Guo, Yubin | Huang, Fuxian*; | Li, Jing
Affiliations: School of Computer, Heze University, Heze 274015, Shandong Province, China
Correspondence: [*] Corresponding author. Fuxian Huang. School of Computer, Heze University, Heze 274015, Shandong Province, China. Email: [email protected].
Abstract: Although some methods of feature extraction for photoplethysmography (PPG) biometric recognition have been extensively studied, effectiveness of local features, time cost of feature extraction, and robust identification for small-scale data remain challenging. To address these issues, we proposed a feature-extraction method of PPG biometrics combining singular value decomposition with local mean decomposition and time-domain parameters. First, we used the singular-value-decomposition method to de-noise the original PPG data. Second, we extracted the local-mean-decomposition-based and time-domain features, which are fused into a concatenated feature. Finally, we combined the concatenated feature with four classifiers for classification and decision-making. Extensive experiments on the three datasets have shown that the waveform of the PPG signal de-noised by singular value decomposition was smoother and more regular, the concatenated feature had strong inter-subject distinguishability and intra-subject similarity, and the concatenated feature combined with a random-forest classifier was the best and could achieve 99.40%, 99.88%, and 99.56% recognition rates on the respective datasets. The method is competitive with several state-of-the-art methods.
Keywords: PPG biometrics, singular value decomposition, local mean decomposition, time-domain parameters
DOI: 10.3233/JIFS-212086
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3599-3610, 2022
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