Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein Recognition
Among the existing biometrics methods, finger-vein recognition is beneficial because finger-veins patterns are locate under the skin and thus difficult to forge. Moreover, user convenience is high because non-invasive image capturing devices are used for recognition. In real environments, however, o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8963699/ |
| _version_ | 1818608428662128640 |
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| author | Jiho Choi Kyoung Jun Noh Se Woon Cho Se Hyun Nam Muhammad Owais Kang Ryoung Park |
| author_facet | Jiho Choi Kyoung Jun Noh Se Woon Cho Se Hyun Nam Muhammad Owais Kang Ryoung Park |
| author_sort | Jiho Choi |
| collection | DOAJ |
| description | Among the existing biometrics methods, finger-vein recognition is beneficial because finger-veins patterns are locate under the skin and thus difficult to forge. Moreover, user convenience is high because non-invasive image capturing devices are used for recognition. In real environments, however, optical blur can occur while capturing finger-vein images du to both skin scattering blur caused by light scattering in the skin layer and lens focus mismatch caused by finger movement. The blurred images generated in this manner can cause severe performance degradation for finger-vein recognition. The majority of the previous studies addressed the restoration method o skin scattering blurred images; however, only limited studies have addressed the restoration of optically blurred images. Even the previous studies on the restoration of optical blur restoration have performed restoration based on the estimation of the accurate point spread function (PSF) for a specific image-capturing device. Thus, it is difficult to apply these methods to finger-vein images acquired by different devices. To address this problem, this paperproposes a new method for restoring optically blurred finger-vei images using a modified conditional generative adversarial network (conditional GAN) and recognizing the restored finger-vein images using a deep convolutional neural network (CNN). The results of the experiment performed using two open databases, the Shandong University homologous multimodal traits (SDUMLA-HMT) finger-vein database and Hong Kong Polytechnic University finger-image database (version 1) confirmed that the proposed method outperforms the existing methods. |
| first_indexed | 2024-12-16T14:42:29Z |
| format | Article |
| id | doaj.art-9d3ef91d48d1474ca6bd1c6617a26a74 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T14:42:29Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9d3ef91d48d1474ca6bd1c6617a26a742022-12-21T22:27:51ZengIEEEIEEE Access2169-35362020-01-018162811630110.1109/ACCESS.2020.29677718963699Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein RecognitionJiho Choi0https://orcid.org/0000-0002-3930-3477Kyoung Jun Noh1https://orcid.org/0000-0001-9782-5030Se Woon Cho2https://orcid.org/0000-0002-1813-9807Se Hyun Nam3https://orcid.org/0000-0002-0181-8774Muhammad Owais4https://orcid.org/0000-0001-7679-081XKang Ryoung Park5https://orcid.org/0000-0002-1214-9510Division of Electronics and Electrical Engineering, Dongguk University, Seoul, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, Seoul, South KoreaAmong the existing biometrics methods, finger-vein recognition is beneficial because finger-veins patterns are locate under the skin and thus difficult to forge. Moreover, user convenience is high because non-invasive image capturing devices are used for recognition. In real environments, however, optical blur can occur while capturing finger-vein images du to both skin scattering blur caused by light scattering in the skin layer and lens focus mismatch caused by finger movement. The blurred images generated in this manner can cause severe performance degradation for finger-vein recognition. The majority of the previous studies addressed the restoration method o skin scattering blurred images; however, only limited studies have addressed the restoration of optically blurred images. Even the previous studies on the restoration of optical blur restoration have performed restoration based on the estimation of the accurate point spread function (PSF) for a specific image-capturing device. Thus, it is difficult to apply these methods to finger-vein images acquired by different devices. To address this problem, this paperproposes a new method for restoring optically blurred finger-vei images using a modified conditional generative adversarial network (conditional GAN) and recognizing the restored finger-vein images using a deep convolutional neural network (CNN). The results of the experiment performed using two open databases, the Shandong University homologous multimodal traits (SDUMLA-HMT) finger-vein database and Hong Kong Polytechnic University finger-image database (version 1) confirmed that the proposed method outperforms the existing methods.https://ieeexplore.ieee.org/document/8963699/Finger-vein recognitionoptical blur image restorationmodified conditional GANCNN |
| spellingShingle | Jiho Choi Kyoung Jun Noh Se Woon Cho Se Hyun Nam Muhammad Owais Kang Ryoung Park Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein Recognition IEEE Access Finger-vein recognition optical blur image restoration modified conditional GAN CNN |
| title | Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein Recognition |
| title_full | Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein Recognition |
| title_fullStr | Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein Recognition |
| title_full_unstemmed | Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein Recognition |
| title_short | Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein Recognition |
| title_sort | modified conditional generative adversarial network based optical blur restoration for finger vein recognition |
| topic | Finger-vein recognition optical blur image restoration modified conditional GAN CNN |
| url | https://ieeexplore.ieee.org/document/8963699/ |
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