| Presentation | 2021-12-16 Low-Resolution Iris Recognition with Image Super-Resolution for arbitrary magnification Tsubasa Bora, Takahiro Toizumi, Yuho Shoji, Yuka Ogino, Masato Tsukada, Masatsugu Ichino, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | A low-resolution iris image reduces iris recognition accuracy. Some conventional researches tackle low-resolution iris recognition using image super-resolution techniques. However, general image super-resolution methods drop personal identity information, and these regard super-resolution of different scales as independent tasks. In this paper, we propose low-resolution iris recognition based on super-resolution of arbitrary scale factors keeping a recognition accuracy. Our method utilizes a probability distribution to control a scale selection during training to suppress differences in recognition performance from different scale super-resolution. We show that our proposed method keeps the recognition accuracy by lower resolution than the conventional methods. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Biometrics / Iris recognition / Deep learning / Image super-resolution / CNN |
| Paper # | PRMU2021-26 |
| Date of Issue | 2021-12-09 (PRMU) |
| Conference Information | |
| Committee | PRMU |
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| Conference Date | 2021/12/16(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Seiichi Uchida(Kyushu Univ.) |
| Vice Chair | Masakazu Iwamura(Osaka Pref. Univ.) / Mitsuru Anpai(Denso IT Lab.) |
| Secretary | Masakazu Iwamura(NTT) / Mitsuru Anpai(Tottori Univ.) |
| Assistant | Kouta Yamaguchi(CyberAgent) / Yusuke Matsui(Univ. of Tokyo) |
| Paper Information | |
| Registration To | Technical Committee on Pattern Recognition and Media Understanding |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Low-Resolution Iris Recognition with Image Super-Resolution for arbitrary magnification |
| Sub Title (in English) | |
| Keyword(1) | Biometrics |
| Keyword(2) | Iris recognition |
| Keyword(3) | Deep learning |
| Keyword(4) | Image super-resolution |
| Keyword(5) | CNN |
| 1st Author's Name | Tsubasa Bora |
| 1st Author's Affiliation | The University of Electro-Communications(UEC) |
| 2nd Author's Name | Takahiro Toizumi |
| 2nd Author's Affiliation | NEC Corporation(NEC) |
| 3rd Author's Name | Yuho Shoji |
| 3rd Author's Affiliation | NEC Corporation(NEC) |
| 4th Author's Name | Yuka Ogino |
| 4th Author's Affiliation | NEC Corporation(NEC) |
| 5th Author's Name | Masato Tsukada |
| 5th Author's Affiliation | NEC Corporation(NEC) |
| 6th Author's Name | Masatsugu Ichino |
| 6th Author's Affiliation | The University of Electro-Communications(UEC) |
| Date | 2021-12-16 |
| Paper # | PRMU2021-26 |
| Volume (vol) | vol.121 |
| Number (no) | PRMU-304 |
| Page | pp.pp.13-18(PRMU), |
| #Pages | 6 |
| Date of Issue | 2021-12-09 (PRMU) |