Illumination-Invariant and Deformation-Tolerant Inner Knuckle Print Recognition Using Portable Devices
We propose a novel biometric recognition method that identifies the inner knuckle print (IKP). It is robust enough to confront uncontrolled lighting conditions, pose variations and low imaging quality. Such robustness is crucial for its application on portable devices equipped with consumer-level ca...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/15/2/4326 |
| _version_ | 1798005504309460992 |
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| author | Xuemiao Xu Qiang Jin Le Zhou Jing Qin Tien-Tsin Wong Guoqiang Han |
| author_facet | Xuemiao Xu Qiang Jin Le Zhou Jing Qin Tien-Tsin Wong Guoqiang Han |
| author_sort | Xuemiao Xu |
| collection | DOAJ |
| description | We propose a novel biometric recognition method that identifies the inner knuckle print (IKP). It is robust enough to confront uncontrolled lighting conditions, pose variations and low imaging quality. Such robustness is crucial for its application on portable devices equipped with consumer-level cameras. We achieve this robustness by two means. First, we propose a novel feature extraction scheme that highlights the salient structure and suppresses incorrect and/or unwanted features. The extracted IKP features retain simple geometry and morphology and reduce the interference of illumination. Second, to counteract the deformation induced by different hand orientations, we propose a novel structure-context descriptor based on local statistics. To our best knowledge, we are the first to simultaneously consider the illumination invariance and deformation tolerance for appearance-based low-resolution hand biometrics. Settings in previous works are more restrictive. They made strong assumptions either about the illumination condition or the restrictive hand orientation. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods in terms of recognition accuracy, especially under uncontrolled lighting conditions and the flexible hand orientation requirement. |
| first_indexed | 2024-04-11T12:41:24Z |
| format | Article |
| id | doaj.art-d1c434f7e9724618ae93d7f6148a2fb7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:41:24Z |
| publishDate | 2015-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d1c434f7e9724618ae93d7f6148a2fb72022-12-22T04:23:30ZengMDPI AGSensors1424-82202015-02-011524326435210.3390/s150204326s150204326Illumination-Invariant and Deformation-Tolerant Inner Knuckle Print Recognition Using Portable DevicesXuemiao Xu0Qiang Jin1Le Zhou2Jing Qin3Tien-Tsin Wong4Guoqiang Han5School of Computer Science and Engineering, South China University of Technology, Higher Education Mega Center, Panyu, Guangzhou 510006, ChinaSchool of Computer Science and Engineering, South China University of Technology, Higher Education Mega Center, Panyu, Guangzhou 510006, ChinaSchool of Computer Science and Engineering, South China University of Technology, Higher Education Mega Center, Panyu, Guangzhou 510006, ChinaNational-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Medicine, Shenzhen University, Shenzhen 518060, ChinaDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong 999077, ChinaSchool of Computer Science and Engineering, South China University of Technology, Higher Education Mega Center, Panyu, Guangzhou 510006, ChinaWe propose a novel biometric recognition method that identifies the inner knuckle print (IKP). It is robust enough to confront uncontrolled lighting conditions, pose variations and low imaging quality. Such robustness is crucial for its application on portable devices equipped with consumer-level cameras. We achieve this robustness by two means. First, we propose a novel feature extraction scheme that highlights the salient structure and suppresses incorrect and/or unwanted features. The extracted IKP features retain simple geometry and morphology and reduce the interference of illumination. Second, to counteract the deformation induced by different hand orientations, we propose a novel structure-context descriptor based on local statistics. To our best knowledge, we are the first to simultaneously consider the illumination invariance and deformation tolerance for appearance-based low-resolution hand biometrics. Settings in previous works are more restrictive. They made strong assumptions either about the illumination condition or the restrictive hand orientation. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods in terms of recognition accuracy, especially under uncontrolled lighting conditions and the flexible hand orientation requirement.http://www.mdpi.com/1424-8220/15/2/4326inner knuckle print recognitionillumination-invariant feature extractiondeformation-tolerant matching |
| spellingShingle | Xuemiao Xu Qiang Jin Le Zhou Jing Qin Tien-Tsin Wong Guoqiang Han Illumination-Invariant and Deformation-Tolerant Inner Knuckle Print Recognition Using Portable Devices Sensors inner knuckle print recognition illumination-invariant feature extraction deformation-tolerant matching |
| title | Illumination-Invariant and Deformation-Tolerant Inner Knuckle Print Recognition Using Portable Devices |
| title_full | Illumination-Invariant and Deformation-Tolerant Inner Knuckle Print Recognition Using Portable Devices |
| title_fullStr | Illumination-Invariant and Deformation-Tolerant Inner Knuckle Print Recognition Using Portable Devices |
| title_full_unstemmed | Illumination-Invariant and Deformation-Tolerant Inner Knuckle Print Recognition Using Portable Devices |
| title_short | Illumination-Invariant and Deformation-Tolerant Inner Knuckle Print Recognition Using Portable Devices |
| title_sort | illumination invariant and deformation tolerant inner knuckle print recognition using portable devices |
| topic | inner knuckle print recognition illumination-invariant feature extraction deformation-tolerant matching |
| url | http://www.mdpi.com/1424-8220/15/2/4326 |
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