Gender Classification Using Video Sequences of Body Sway Recorded by Overhead Camera
[Abstract]
We investigate whether it is possible to classify the gender of a standing person based on a video sequence containing body sway recorded by an overhead camera.
Existing methods that extract a feature from the movement of a walking person for gender classification cannot detect the slight movements of a standing person.
In this paper, we propose a method for extracting a feature from the body sway of a standing person.
We design a spatio-temporal feature for representing body sway using the frequency analysis of time-series signals derived from the local movements of the upper body.
To evaluate the accuracy of our method, we acquired video sequences of body sway from 30 females and 30 males using an overhead camera.
We found that our method obtained 90.3% accuracy for the gender classification of a standing person.
We compared the accuracy of our method with that of parameters based on medical data.
We found that the proposed spatio-temporal feature extracted from body sway significantly improved gender classification accuracy.
[Publications]
- 神谷 卓也, 山口 優太, 中谷 真太朗, 西山 正志, 岩井 儀雄,
頭上カメラから観測された身体動揺を用いた性別認識の精度評価,
映像情報メディア学会誌, Vol. 75, No. 3, pp. 438 - 447, May 2021.
- Takuya Kamitani, Yuta Yamaguchi, Shintaro Nakatani, Masashi Nishiyama, Yoshio Iwai,
Gender Classification Using Video Sequences of Body Sway Recorded by Overhead Camera,
Proceedings of 25th International Conference on Pattern Recognition (ICPR), pp. 9196 - 9202, January 2021.
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