PPG biometric recognition with singular value decomposition and local mean decomposition

被引:1
|
作者
Yang, Junfeng [1 ]
Huang, Yuwen [1 ]
Guo, Yubin [1 ]
Huang, Fuxian [1 ]
Li, Jing [1 ]
机构
[1] Heze Univ, Sch Comp, Heze 274015, Shandong, Peoples R China
关键词
PPG biometrics; singular value decomposition; local mean decomposition; time-domain parameters;
D O I
10.3233/JIFS-212086
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:3599 / 3610
页数:12
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