| Presentation | 2021-03-02 Improving an Accuracy of Personal Identification Using Ensemble Learning and Footsteps Yoshiki Goto, Akitoshi Itai, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | It is known that the footstep includes personal characteristics. We often recognize a person from walking footsteps in limited situation. If the high accuracy personal identification using footstep is possible, a novel surveillance system like a crime prevention system, or a biometric system are expected. We showed that the ResNet of CNN trained by footstep waveform images performs the identification accuracy of 95.8% for 10 subjects. Pan reported that the footstep identification using 7 steps of vibration data and ITSVM with the accuracy of 96.0% for 10 subjects. However, the accuracy of these researches is not enough to use of a biometric system. In this paper, we propose a high time resolution dataset. In addition, we apply an ensemble learning using three datasets to achieve more accurate personal identification. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Footstep / Ensemble learning / CNN / Personal identification |
| Paper # | BioX2020-41,CNR2020-14 |
| Date of Issue | 2021-02-23 (BioX, CNR) |
| Conference Information | |
| Committee | BioX / CNR |
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| Conference Date | 2021/3/2(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Akira Otsuka(AIST) / Kazunori Takashio(Keio Univ.) |
| Vice Chair | Takahiro Aoki(Fujitsu Labs.) / Masatsugu Ichino(Univ. of Electro-Comm.) / Masayuki Kanbara(NAIST) / Yoshihiko Murakawa(Fujitsu Labs.) |
| Secretary | Takahiro Aoki(SECOM) / Masatsugu Ichino(KDDI Research) / Masayuki Kanbara(Shibaura Inst. of Tech.) / Yoshihiko Murakawa(Panasonic) |
| Assistant | Emiko Sano(MitsubishiElectric) / Akihiro Hayasaka(NEC) / Yuka Kobayashi(Toshiba) / Masanori Yokoyama(ＮＴＴ) |
| Paper Information | |
| Registration To | Technical Committee on Biometrics / Technical Committee on Cloud Network Robotics |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Improving an Accuracy of Personal Identification Using Ensemble Learning and Footsteps |
| Sub Title (in English) | |
| Keyword(1) | Footstep |
| Keyword(2) | Ensemble learning |
| Keyword(3) | CNN |
| Keyword(4) | Personal identification |
| 1st Author's Name | Yoshiki Goto |
| 1st Author's Affiliation | Chubu University(Chubu Univ.) |
| 2nd Author's Name | Akitoshi Itai |
| 2nd Author's Affiliation | Chubu University(Chubu Univ.) |
| Date | 2021-03-02 |
| Paper # | BioX2020-41,CNR2020-14 |
| Volume (vol) | vol.120 |
| Number (no) | BioX-393,CNR-394 |
| Page | pp.pp.7-11(BioX), pp.7-11(CNR), |
| #Pages | 5 |
| Date of Issue | 2021-02-23 (BioX, CNR) |