Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition
Palmprint recognition has been widely studied for security applications. However, there is a lack of in-depth investigations on robust palmprint recognition. Regression analysis being intuitively interpretable on robustness design inspires us to propose a correntropy-induced discriminative nonnegati...
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| Format: | Article |
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MDPI AG
2020-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/15/4250 |
| _version_ | 1797560804735713280 |
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| author | Kunlei Jing Xinman Zhang Guokun Song |
| author_facet | Kunlei Jing Xinman Zhang Guokun Song |
| author_sort | Kunlei Jing |
| collection | DOAJ |
| description | Palmprint recognition has been widely studied for security applications. However, there is a lack of in-depth investigations on robust palmprint recognition. Regression analysis being intuitively interpretable on robustness design inspires us to propose a correntropy-induced discriminative nonnegative sparse coding method for robust palmprint recognition. Specifically, we combine the correntropy metric and <i>l</i><sub>1</sub>-norm to present a powerful error estimator that gains flexibility and robustness to various contaminations by cooperatively detecting and correcting errors. Furthermore, we equip the error estimator with a tailored discriminative nonnegative sparse regularizer to extract significant nonnegative features. We manage to explore an analytical optimization approach regarding this unified scheme and figure out a novel efficient method to address the challenging non-negative constraint. Finally, the proposed coding method is extended for robust multispectral palmprint recognition. Namely, we develop a constrained particle swarm optimizer to search for the feasible parameters to fuse the extracted robust features of different spectrums. Extensive experimental results on both contactless and contact-based multispectral palmprint databases verify the flexibility and robustness of our methods. |
| first_indexed | 2024-03-10T18:05:37Z |
| format | Article |
| id | doaj.art-d83248a4c7a64b1d813de67ae02c187a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:05:37Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d83248a4c7a64b1d813de67ae02c187a2023-11-20T08:31:37ZengMDPI AGSensors1424-82202020-07-012015425010.3390/s20154250Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint RecognitionKunlei Jing0Xinman Zhang1Guokun Song2School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an 710049, ChinaSichuan Gas Turbine Research Institute of AVIC, No. 6 Xinjun Road, Xindu District, Chengdu 610500, ChinaPalmprint recognition has been widely studied for security applications. However, there is a lack of in-depth investigations on robust palmprint recognition. Regression analysis being intuitively interpretable on robustness design inspires us to propose a correntropy-induced discriminative nonnegative sparse coding method for robust palmprint recognition. Specifically, we combine the correntropy metric and <i>l</i><sub>1</sub>-norm to present a powerful error estimator that gains flexibility and robustness to various contaminations by cooperatively detecting and correcting errors. Furthermore, we equip the error estimator with a tailored discriminative nonnegative sparse regularizer to extract significant nonnegative features. We manage to explore an analytical optimization approach regarding this unified scheme and figure out a novel efficient method to address the challenging non-negative constraint. Finally, the proposed coding method is extended for robust multispectral palmprint recognition. Namely, we develop a constrained particle swarm optimizer to search for the feasible parameters to fuse the extracted robust features of different spectrums. Extensive experimental results on both contactless and contact-based multispectral palmprint databases verify the flexibility and robustness of our methods.https://www.mdpi.com/1424-8220/20/15/4250robust palmprint recognitionregression analysiscorrentropy metricdiscriminative nonnegative regularizernonnegative constraintconstrained particle swarm optimizer |
| spellingShingle | Kunlei Jing Xinman Zhang Guokun Song Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition Sensors robust palmprint recognition regression analysis correntropy metric discriminative nonnegative regularizer nonnegative constraint constrained particle swarm optimizer |
| title | Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition |
| title_full | Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition |
| title_fullStr | Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition |
| title_full_unstemmed | Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition |
| title_short | Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition |
| title_sort | correntropy induced discriminative nonnegative sparse coding for robust palmprint recognition |
| topic | robust palmprint recognition regression analysis correntropy metric discriminative nonnegative regularizer nonnegative constraint constrained particle swarm optimizer |
| url | https://www.mdpi.com/1424-8220/20/15/4250 |
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