講演抄録/キーワード |
講演名 |
2018-12-25 15:00
Human Gait Recognition Method for Long and Baggy Clothes Using Machine Learning ○Abrar Alharbi・Eiji Kamioka(SIT) MoNA2018-50 |
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. |
(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 / / / |
(英) |
Gait Recognition / Biometric Systems / Kinect Sensor / Model-based System / Machine Learning / / / |
文献情報 |
信学技報, vol. 118, no. 378, MoNA2018-50, pp. 67-72, 2018年12月. |
資料番号 |
MoNA2018-50 |
発行日 |
2018-12-18 (MoNA) |
ISSN |
Online edition: ISSN 2432-6380 |
著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
PDFダウンロード |
MoNA2018-50 |