| 講演名 | 2018-12-25 Human Gait Recognition Method for Long and Baggy Clothes Using Machine Learning Abrar Alharbi(SIT), Eiji Kamioka(SIT), |
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| 抄録(和) | Human gait is a significant biometric feature used for the identification of people by their style of walking. In comparison with other methods of biometric information, gait offers recognition from a distance at low resolution while requiring no user interaction, whereas other biometrics are likely to require a certain level of interaction. In This paper, a human gait recognition method is presented to identify people who are wearing long baggy clothes like Thoub and Abaya. Microsoft Kinect sensor is used as a tool to establish a skeleton based gait database. The skeleton joint positions are obtained and used as input parameters for classification. An evaluation experiment was carried out with 15 walking subjects, each having 30 walking sequences in total, achieving the recognition rate of 100% using Support Vector Machine classifier. |
| 抄録(英) | Human gait is a significant biometric feature used for the identification of people by their style of walking. In comparison with other methods of biometric information, gait offers recognition from a distance at low resolution while requiring no user interaction, whereas other biometrics are likely to require a certain level of interaction. In This paper, a human gait recognition method is presented to identify people who are wearing long baggy clothes like Thoub and Abaya. Microsoft Kinect sensor is used as a tool to establish a skeleton based gait database. The skeleton joint positions are obtained and used as input parameters for classification. An evaluation experiment was carried out with 15 walking subjects, each having 30 walking sequences in total, achieving the recognition rate of 100% using Support Vector Machine classifier. |
| キーワード(和) | Gait Recognition / Biometric Systems / Kinect Sensor / Model-based System / Machine Learning |
| キーワード(英) | Gait Recognition / Biometric Systems / Kinect Sensor / Model-based System / Machine Learning |
| 資料番号 | MoNA2018-50 |
| 発行日 | 2018-12-18 (MoNA) |
| 研究会情報 | |
| 研究会 | MoNA |
|---|---|
| 開催期間 | 2018/12/25(から1日開催) |
| 開催地（和） | 芝浦工大豊洲キャンパス |
| 開催地（英） | |
| テーマ（和） | 一般 |
| テーマ（英） | General |
| 委員長氏名（和） | 新熊 亮一(京大) |
| 委員長氏名（英） | Ryoichi Shinkuma(Kyoto Univ.) |
| 副委員長氏名（和） | 田頭 茂明(関大) / 北形 元(東北大) |
| 副委員長氏名（英） | Shigeaki Tagashira(Kansai Univ.) / Gen Kitagata(Tohoku Univ.) |
| 幹事氏名（和） | 石田 繁巳(九大) / 二瓶 浩一(NEC) / 西尾 理志(京大) / 齊藤 隆仁(NTTドコモ) |
| 幹事氏名（英） | Shigemi Ishida(Kyushu Univ.) / Koichi Nihei(NEC) / Takayuki Nishio(Kyoto Univ.) / Takato Saito(NTT DOCOMO) |
| 幹事補佐氏名（和） | 臼井 健(KDDI総合研究所) / 金井 謙治(早大) |
| 幹事補佐氏名（英） | Ken Usui(KDDI Research) / Kenji Kanai(Waseda Univ.) |
| 講演論文情報詳細 | |
| 申込み研究会 | Technical Committee on Mobile Network and Applications |
|---|---|
| 本文の言語 | ENG |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Human Gait Recognition Method for Long and Baggy Clothes Using Machine Learning |
| サブタイトル（和） | |
| キーワード(1)（和/英） | Gait Recognition / Gait Recognition |
| キーワード(2)（和/英） | Biometric Systems / Biometric Systems |
| キーワード(3)（和/英） | Kinect Sensor / Kinect Sensor |
| キーワード(4)（和/英） | Model-based System / Model-based System |
| キーワード(5)（和/英） | Machine Learning / Machine Learning |
| 第 1 著者 氏名（和/英） | Abrar Alharbi / Abrar Alharbi |
| 第 1 著者 所属（和/英） | Shibaura Institute of Technology(略称：SIT) Shibaura Institute of Technology(略称：SIT) |
| 第 2 著者 氏名（和/英） | Eiji Kamioka / Eiji Kamioka |
| 第 2 著者 所属（和/英） | Shibaura Institute of Technology(略称：SIT) Shibaura Institute of Technology(略称：SIT) |
| 発表年月日 | 2018-12-25 |
| 資料番号 | MoNA2018-50 |
| 巻番号（vol） | vol.118 |
| 号番号（no） | MoNA-378 |
| ページ範囲 | pp.67-72(MoNA), |
| ページ数 | 6 |
| 発行日 | 2018-12-18 (MoNA) |