Cross-view Gait-based Gender Classification by Transfer Learning

	Cross-view Gait-based Gender Classification by Transfer Learning
	Zhenjun Yao; Zhaoxiang Zhang; Maodi Hu; Yunhong Wang
	2013-12-13
会议日期	13-16 December 2013
会议地点	Nanjing, China
关键词	Gait-based Gender Classification Cross-view Transfer Learning
英文摘要	The gender of a person is easily recognized by his/her gait when training data and test data are from the same view. However, when it comes to cross-view gender classification, traditional methods can not deal with large view variation without enough labeled data in the target view. In this paper, we solve this problem by introducing a transfer learning based framework. Firstly, Gait Energy Image (GEI) of each gait sequence for all views is generated, and Principal Component Analysis (PCA) is carried out to obtain efficient gait representations. Subsequently, an inductive transfer learning approach, TrAdaBoost, is adopted to transfer knowledge from the source view to the target view, which significantly improves the performance of gait-based gender classification. Abundant experiments are conducted and experimental results demonstrate the superiority of the proposed method over traditional gait analysis methods.
会议录	PCM 2013
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/13285]
专题	自动化研究所_类脑智能研究中心
通讯作者	Zhaoxiang Zhang
推荐引用方式 GB/T 7714	Zhenjun Yao,Zhaoxiang Zhang,Maodi Hu,et al. Cross-view Gait-based Gender Classification by Transfer Learning[C]. 见:. Nanjing, China. 13-16 December 2013.