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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],2013.
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