Palmprint-Palmvein Fusion Recognition Based on Deep Hashing Network
Palmprint has attracted increasing attention due to its several advantages in the biometrics field. Deep learning has achieved remarkable performance in the computer vision area, so a large number of deep-learning-based methods have been proposed by the research community for palmprint recognition....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9536698/ |
| _version_ | 1818196459474911232 |
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| author | Tengfei Wu Lu Leng Muhammad Khurram Khan Farrukh Aslam Khan |
| author_facet | Tengfei Wu Lu Leng Muhammad Khurram Khan Farrukh Aslam Khan |
| author_sort | Tengfei Wu |
| collection | DOAJ |
| description | Palmprint has attracted increasing attention due to its several advantages in the biometrics field. Deep learning has achieved remarkable performance in the computer vision area, so a large number of deep-learning-based methods have been proposed by the research community for palmprint recognition. The outputs of a deep hashing network (DHN) can be represented as a binary bit string, so DHN can reduce the storage and accelerate the matching/retrieval speed. In this paper, DHN is employed to extract the binary template for palmprint and palmvein verification. Spatial transformer network is used to overcome the rotation and dislocation. Palmprint and palmvein can be acquired from visible-light spectrums, including red (R), green (G), blue (B), and near infrared (NIR) spectrum, respectively. Since the features in different spectrums are different, their complementary advantages can be exploited to the full by fusion. Image-level fusion and score-level fusion are developed for palmprint-palmvein fusion recognition. The experiments demonstrate that score-level fusion can improve the accuracy efficiently. |
| first_indexed | 2024-12-12T01:34:25Z |
| format | Article |
| id | doaj.art-b019b3b1a9974d3a92933992cdbe0a4d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T01:34:25Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b019b3b1a9974d3a92933992cdbe0a4d2022-12-22T00:42:53ZengIEEEIEEE Access2169-35362021-01-01913581613582710.1109/ACCESS.2021.31125139536698Palmprint-Palmvein Fusion Recognition Based on Deep Hashing NetworkTengfei Wu0https://orcid.org/0000-0001-8924-5749Lu Leng1https://orcid.org/0000-0002-5667-224XMuhammad Khurram Khan2https://orcid.org/0000-0001-6636-0533Farrukh Aslam Khan3https://orcid.org/0000-0002-7023-7172School of Software, Nanchang Hangkong University, Nanchang, ChinaSchool of Software, Nanchang Hangkong University, Nanchang, ChinaCenter of Excellence in Information Assurance, King Saud University, Riyadh, Saudi ArabiaCenter of Excellence in Information Assurance, King Saud University, Riyadh, Saudi ArabiaPalmprint has attracted increasing attention due to its several advantages in the biometrics field. Deep learning has achieved remarkable performance in the computer vision area, so a large number of deep-learning-based methods have been proposed by the research community for palmprint recognition. The outputs of a deep hashing network (DHN) can be represented as a binary bit string, so DHN can reduce the storage and accelerate the matching/retrieval speed. In this paper, DHN is employed to extract the binary template for palmprint and palmvein verification. Spatial transformer network is used to overcome the rotation and dislocation. Palmprint and palmvein can be acquired from visible-light spectrums, including red (R), green (G), blue (B), and near infrared (NIR) spectrum, respectively. Since the features in different spectrums are different, their complementary advantages can be exploited to the full by fusion. Image-level fusion and score-level fusion are developed for palmprint-palmvein fusion recognition. The experiments demonstrate that score-level fusion can improve the accuracy efficiently.https://ieeexplore.ieee.org/document/9536698/Biometric recognitionpalmprint verificationpalmvein verificationfusion recognitiondeep hashing network |
| spellingShingle | Tengfei Wu Lu Leng Muhammad Khurram Khan Farrukh Aslam Khan Palmprint-Palmvein Fusion Recognition Based on Deep Hashing Network IEEE Access Biometric recognition palmprint verification palmvein verification fusion recognition deep hashing network |
| title | Palmprint-Palmvein Fusion Recognition Based on Deep Hashing Network |
| title_full | Palmprint-Palmvein Fusion Recognition Based on Deep Hashing Network |
| title_fullStr | Palmprint-Palmvein Fusion Recognition Based on Deep Hashing Network |
| title_full_unstemmed | Palmprint-Palmvein Fusion Recognition Based on Deep Hashing Network |
| title_short | Palmprint-Palmvein Fusion Recognition Based on Deep Hashing Network |
| title_sort | palmprint palmvein fusion recognition based on deep hashing network |
| topic | Biometric recognition palmprint verification palmvein verification fusion recognition deep hashing network |
| url | https://ieeexplore.ieee.org/document/9536698/ |
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