PPG biometric recognition with singular value decomposition and local mean decomposition

• Published in: Journal of intelligent & fuzzy systems Vol. 43; no. 3; p. 3599
• Main Authors: Yang, Junfeng, Huang, Yuwen, Guo, Yubin, Huang, Fuxian, Li, Jing
• Format: Journal Article
• Language: English
• Published: London Sage Publications Ltd 01.01.2022
• Subjects: Biometrics; Classifiers; Datasets; Decision making; Decision trees; Decomposition; Feature extraction; Singular value decomposition; Time domain analysis; Waveforms
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-212086

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.