Locally regularized sliced inverse regression based 3D hand gesture recognition on a dance robot

  • Authors:

  • Jun Cheng;Wei Bian;Dacheng Tao

  • Affiliations:

  • Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China and The Chinese University of Hong Kong, Hong Kong, China and Guangdong Provincial Key Laboratory of Roboti ...;Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, 235 Jones Street, Ultimo, NSW 2007, Australia;Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, 235 Jones Street, Ultimo, NSW 2007, Australia

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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Abstract

Gesture recognition plays an important role in human machine interactions (HMIs) for multimedia entertainment. In this paper, we present a dimension reduction based approach for dynamic real-time hand gesture recognition. The hand gestures are recorded as acceleration signals by using a handheld with a 3-axis accelerometer sensor installed, and represented by discrete cosine transform (DCT) coefficients. To recognize different hand gestures, we develop a new dimension reduction method, locally regularized sliced inverse regression (LR-SIR), to find an effective low dimensional subspace, in which different hand gestures are well separable, following which recognition can be performed by using simple and efficient classifiers, e.g., nearest mean, k-nearest-neighbor rule and support vector machine. LR-SIR is built upon the well-known sliced inverse regression (SIR), but overcomes its limitation that it ignores the local geometry of the data distribution. Besides, LR-SIR can be effectively and efficiently solved by eigen-decomposition. Finally, we apply the LR-SIR based gesture recognition to control our recently developed dance robot for multimedia entertainment. Thorough empirical studies on 'digits'-gesture recognition suggest the effectiveness of the new gesture recognition scheme for HMI.