Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor
Photoplethysmography (PPG) biometric recognition has recently received considerable attention and is considered to be a promising biometric trait. Although some promising results on PPG biometric recognition have been reported, challenges in noise sensitivity and poor robustness remain. To address t...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/9653470 |
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| author | Junfeng Yang Yuwen Huang Fuxian Huang Gongping Yang |
| author_facet | Junfeng Yang Yuwen Huang Fuxian Huang Gongping Yang |
| author_sort | Junfeng Yang |
| collection | DOAJ |
| description | Photoplethysmography (PPG) biometric recognition has recently received considerable attention and is considered to be a promising biometric trait. Although some promising results on PPG biometric recognition have been reported, challenges in noise sensitivity and poor robustness remain. To address these issues, a PPG biometric recognition framework is presented in this article, that is, a PPG biometric recognition model based on a sparse softmax vector and k-nearest neighbor. First, raw PPG data are rerepresented by sliding window scanning. Second, three-layer features are extracted, and the features of each layer are represented by a sparse softmax vector. In the first layer, the features are extracted by PPG data as a whole. In the second layer, all the PPG data are divided into four subregions, then four subfeatures are generated by extracting features from the four subregions, and finally, the four subfeatures are averaged as the second layer features. In the third layer, all the PPG data are divided into 16 subregions, then 16 subfeatures are generated by extracting features from the 16 subregions, and finally, the 16 subfeatures are averaged as the third layer features. Finally, the features with first, second, and third layers are combined into three-layer features. Extensive experiments were conducted on three PPG datasets, and it was found that the proposed method can achieve a recognition rate of 99.95%, 97.21%, and 99.92% on the respective sets. The results demonstrate that the proposed method can outperform current state-of-the-art methods in terms of accuracy. |
| format | Article |
| id | doaj-art-623b3e16ccd648c9ba95d94d5d523e52 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-623b3e16ccd648c9ba95d94d5d523e522025-02-03T01:00:19ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552020-01-01202010.1155/2020/96534709653470Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest NeighborJunfeng Yang0Yuwen Huang1Fuxian Huang2Gongping Yang3School of Computer, Heze University, Heze 274015, ChinaSchool of Computer, Heze University, Heze 274015, ChinaSchool of Computer, Heze University, Heze 274015, ChinaSchool of Software, Shandong University, Jinan 250101, ChinaPhotoplethysmography (PPG) biometric recognition has recently received considerable attention and is considered to be a promising biometric trait. Although some promising results on PPG biometric recognition have been reported, challenges in noise sensitivity and poor robustness remain. To address these issues, a PPG biometric recognition framework is presented in this article, that is, a PPG biometric recognition model based on a sparse softmax vector and k-nearest neighbor. First, raw PPG data are rerepresented by sliding window scanning. Second, three-layer features are extracted, and the features of each layer are represented by a sparse softmax vector. In the first layer, the features are extracted by PPG data as a whole. In the second layer, all the PPG data are divided into four subregions, then four subfeatures are generated by extracting features from the four subregions, and finally, the four subfeatures are averaged as the second layer features. In the third layer, all the PPG data are divided into 16 subregions, then 16 subfeatures are generated by extracting features from the 16 subregions, and finally, the 16 subfeatures are averaged as the third layer features. Finally, the features with first, second, and third layers are combined into three-layer features. Extensive experiments were conducted on three PPG datasets, and it was found that the proposed method can achieve a recognition rate of 99.95%, 97.21%, and 99.92% on the respective sets. The results demonstrate that the proposed method can outperform current state-of-the-art methods in terms of accuracy.http://dx.doi.org/10.1155/2020/9653470 |
| spellingShingle | Junfeng Yang Yuwen Huang Fuxian Huang Gongping Yang Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor Journal of Electrical and Computer Engineering |
| title | Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor |
| title_full | Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor |
| title_fullStr | Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor |
| title_full_unstemmed | Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor |
| title_short | Photoplethysmography Biometric Recognition Model Based on Sparse Softmax Vector and k-Nearest Neighbor |
| title_sort | photoplethysmography biometric recognition model based on sparse softmax vector and k nearest neighbor |
| url | http://dx.doi.org/10.1155/2020/9653470 |
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