@inproceedings{a33c8adfb6d54c92aaa2211ed12e4c30,
title = "A new approach for gender classification based on gait analysis",
abstract = "In this paper, we propose a novel pattern to represent spatio-temporal information of gait appearance which is called Gait Principal Component Image (GPCI). GPCI is a grey-level image which compresses the spatiotemporal information by amplifying the dynamic variation of different body part. The detection of gait period is based on LLE coefficients and it is also a new attempt. KNN classifier is employed for gender classification. The framework can be applied in real-time setting because of its rapidity and robustness. The experimental results on IRIP Gait Database (32 males, 28 females) show that the proposed approach achieves a high accuracy in automatic gender classification.",
keywords = "Gait, Gender classification, LLE coefficients, Principal component",
author = "Maodi Hu and Yunhong Wang",
year = "2009",
doi = "10.1109/ICIG.2009.94",
language = "英语",
isbn = "9780769538839",
series = "Proceedings of the 5th International Conference on Image and Graphics, ICIG 2009",
publisher = "IEEE Computer Society",
pages = "869--874",
booktitle = "Proceedings of the 5th International Conference on Image and Graphics, ICIG 2009",
address = "美国",
note = "5th International Conference on Image and Graphics, ICIG 2009 ; Conference date: 20-09-2009 Through 23-09-2009",
}