Online PCA with adaptive subspace method for real-time hand gesture learning and recognition

  • Authors:

  • Minghai Yao;Xinyu Qu;Qinlong Gu;Taotao Ruan;Zhongwang Lou

  • Affiliations:

  • College of Information Engineering, Zhejiang University of Technology, Hangzhou, China;College of Information Engineering, Zhejiang University of Technology, Hangzhou, China;College of Information Engineering, Zhejiang University of Technology, Hangzhou, China;College of Information Engineering, Zhejiang University of Technology, Hangzhou, China;College of Information Engineering, Zhejiang University of Technology, Hangzhou, China

  • Venue:
  • WSEAS Transactions on Computers
  • Year:
  • 2010

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Abstract

The learning method for hand gesture recognition that compute a space of eigenvectors by Principal Component Analysis(PCA) traditionally require a batch computation step, in which the only way to update the subspace is to rebuild the subspace by the scratch when it comes to new samples. In this paper, we introduce a new approach to gesture recognition based on online PCA algorithm with adaptive subspace, which allows for complete incremental learning. We propose to use different subspace updating strategy for new sample according to the degree of difference between new sample and learned sample, which can improve the adaptability in different situations, and also reduce the time of calculation and storage space. The experimental results show that the proposed method can recognize the unknown hand gesture, realizing online hand gesture accumulation and updating, and improving the recognition performance of system.