Cross-view Gait-based Gender Classification by Transfer Learning

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	Cross-view Gait-based Gender Classification by Transfer Learning
	Zhenjun Yao; Zhaoxiang Zhang; Maodi Hu; Yunhong Wang
	2013-12-13
会议名称	14th Pacific-Rim Conference On Multimedia
会议录名称	PCM 2013
会议日期	13-16 December 2013
会议地点	Nanjing, China
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.
关键词	Gait-based Gender Classification Cross-view Transfer Learning
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13285
专题	智能感知与计算研究中心
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],2013.