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Cross-view Gait-based Gender Classification by Transfer Learning
Zhenjun Yao; Zhaoxiang Zhang; Maodi Hu; Yunhong Wang
Conference Name14th Pacific-Rim Conference On Multimedia
Source PublicationPCM 2013
Conference Date13-16 December 2013
Conference PlaceNanjing, China
AbstractThe 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.
KeywordGait-based Gender Classification Cross-view Transfer Learning
Document Type会议论文
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
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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