Palmprint recognition in uncontrolled and uncooperative environment
Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-...
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| Format: | Journal Article |
| Language: | English |
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2022
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| Online Access: | https://hdl.handle.net/10356/161245 |
| _version_ | 1826119037966352384 |
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| author | Matkowski, Wojciech Michal Chai, Tingting Kong, Adams Wai Kin |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Matkowski, Wojciech Michal Chai, Tingting Kong, Adams Wai Kin |
| author_sort | Matkowski, Wojciech Michal |
| collection | NTU |
| description | Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic
investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new
palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three
public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods. |
| first_indexed | 2024-10-01T04:53:29Z |
| format | Journal Article |
| id | ntu-10356/161245 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:53:29Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1612452022-08-22T06:11:37Z Palmprint recognition in uncontrolled and uncooperative environment Matkowski, Wojciech Michal Chai, Tingting Kong, Adams Wai Kin School of Computer Science and Engineering Engineering::Computer science and engineering Biometrics Palmprint Recognition Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods. Ministry of Education (MOE) This work was supported in part by the Ministry of Education, Singapore through Academic Research Fund Tier 2, under Grant MOE2016-T2-1-042(S). 2022-08-22T06:11:37Z 2022-08-22T06:11:37Z 2019 Journal Article Matkowski, W. M., Chai, T. & Kong, A. W. K. (2019). Palmprint recognition in uncontrolled and uncooperative environment. IEEE Transactions On Information Forensics and Security, 15, 1601-1615. https://dx.doi.org/10.1109/TIFS.2019.2945183 1556-6013 https://hdl.handle.net/10356/161245 10.1109/TIFS.2019.2945183 2-s2.0-85072973472 15 1601 1615 en MOE2016-T2-1-042(S) IEEE Transactions on Information Forensics and Security © 2019 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Biometrics Palmprint Recognition Matkowski, Wojciech Michal Chai, Tingting Kong, Adams Wai Kin Palmprint recognition in uncontrolled and uncooperative environment |
| title | Palmprint recognition in uncontrolled and uncooperative environment |
| title_full | Palmprint recognition in uncontrolled and uncooperative environment |
| title_fullStr | Palmprint recognition in uncontrolled and uncooperative environment |
| title_full_unstemmed | Palmprint recognition in uncontrolled and uncooperative environment |
| title_short | Palmprint recognition in uncontrolled and uncooperative environment |
| title_sort | palmprint recognition in uncontrolled and uncooperative environment |
| topic | Engineering::Computer science and engineering Biometrics Palmprint Recognition |
| url | https://hdl.handle.net/10356/161245 |
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