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表面筋電位による個人認証システムへの深層学習適用の試み
http://hdl.handle.net/10458/00010288
http://hdl.handle.net/10458/0001028817b27abc-d7d0-4539-afd9-8a7d1aeda108
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
本文 (1.7 MB)
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||
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| 公開日 | 2021-11-04 | |||||
| タイトル | ||||||
| タイトル | 表面筋電位による個人認証システムへの深層学習適用の試み | |||||
| 言語 | ja | |||||
| タイトル | ||||||
| タイトル | On an Attempt at Application of Deep Learning to a User Authentication Method Using s-EMG | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | jpn | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Mobile device | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | user authentication | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | shoulder surfing | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | s-EMG | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | deep learning | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | departmental bulletin paper | |||||
| 著者 |
山場, 久昭
× 山場, 久昭× 白石, 堅哉× 油田, 健太郎× 岡崎, 直宣× Shiraishi, Kenya |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In our present era, mobile devices such as tablet-type personal computers (PCs) and smartphones have penetrated deeply into our daily lives. We report on a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals rather than screen-touch operations. These s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, can be used to identify who generated the signals and which gestures were made. Our method uses a technique called “pass-gesture,” which refers to a series of hand gestures, to achieve s-EMG-based authentication. In this paper, deep learning is introduced to facilitate the identification of gestures from s-EMG signals. Pictures of s-EMG signals were directly used to differentiate gestures in experiments and the performance was evaluated. The results showed that the performance using deep learning was not better than the one using support vector machines that was used in our previous works. However, we expect the results to be used as one of the benchmark indices for the future improvement. | |||||
| 言語 | en | |||||
| 書誌情報 |
ja : 宮崎大学工学部紀要 en : Memoirs of Faculty of Engineering, University of Miyazaki 巻 50, p. 193-197, 発行日 2021-09-28 |
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| 出版者 | ||||||
| 出版者 | 宮崎大学工学部 | |||||
| 言語 | ja | |||||
| 出版者 | ||||||
| 出版者 | Faculty of Engineering, University of Miyazaki | |||||
| 言語 | en | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 05404924 | |||||
| 書誌レコードID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA00732558 | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
