| Presentation | 2017-10-12 Improvement of Accuracy by Machine Learning for Personal Authentication using High Frequency Intra-Body Propagation Characteristics Shun Onoda, Takahiro Yoshida, Seiichiro Hangai, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | As one of biometrics that can perform continuous personal authentication only by handling equipment, the personal authentication method using intra-body-frequency characteristics between two fingers have been researching in our laboratory. However, in our previous study, the authentication accuracy with verification method using the Manhattan distance for spectra of the intra-body-frequency characteristics (pass-through / reflection) measured by VNA was very low, e.g. the equal error rate (EER) of the verification using reflection characteristic S22 by the nine subjects was 25.1%. Therefore, in this study, we applied logistic regression, which is one of machine learning method, to the verification in order to improve the verification performance. As a result, the 5.8% EER was archived by applying the logistic regression, that was 19.3 points improvement. It was found that the logistic regression was also effective in the verification using intra-body-propagation characteristics. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Biometrics / intra-body-propagation characteristics / logistic regression |
| Paper # | BioX2017-26 |
| Date of Issue | 2017-10-05 (BioX) |
| Conference Information | |
| Committee | BioX |
|---|---|
| Conference Date | 2017/10/12(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nobumoto Ohama Memorial Hall |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Biometrics, etc. |
| Chair | Kazuhiko Sumi(AGU) |
| Vice Chair | Hiroshi Takano(Toyama Pref. Univ.) / Hitoshi Imaoka(NEC) |
| Secretary | Hiroshi Takano(AIST) / Hitoshi Imaoka(Fujitsu Labs.) |
| Assistant | Masatsugu Ichino(Univ. of Electro-Comm.) / Naoyuki Takada(Secom) / Norihiro Okui(KDDI Research) |
| Paper Information | |
| Registration To | Technical Committee on Biometrics |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Improvement of Accuracy by Machine Learning for Personal Authentication using High Frequency Intra-Body Propagation Characteristics |
| Sub Title (in English) | |
| Keyword(1) | Biometrics |
| Keyword(2) | intra-body-propagation characteristics |
| Keyword(3) | logistic regression |
| 1st Author's Name | Shun Onoda |
| 1st Author's Affiliation | Tokyo University of Science(TUS) |
| 2nd Author's Name | Takahiro Yoshida |
| 2nd Author's Affiliation | Tokyo University of Science(TUS) |
| 3rd Author's Name | Seiichiro Hangai |
| 3rd Author's Affiliation | Tokyo University of Science(TUS) |
| Date | 2017-10-12 |
| Paper # | BioX2017-26 |
| Volume (vol) | vol.117 |
| Number (no) | BioX-236 |
| Page | pp.pp.7-10(BioX), |
| #Pages | 4 |
| Date of Issue | 2017-10-05 (BioX) |