Feature alignment approach for hand posture recognition based on curvature scale space

  • Authors:
  • Chin-Chen Chang;Cheng-Yi Liu;Wen-Kai Tai

  • Affiliations:
  • Department of Computer Science and Information Engineering, National United University, 1, Lien Da, Kungching Li, Miaoli 360, Taiwan;Advanced Technology Center, Computer and Communications Research Laboratories, Industrial Technology Research Institute, Chutung, Hsinchu 310, Taiwan;Department of Computer Science and Information Engineering, National Dong Hwa University, Shou-Feng, Hualien 974, Taiwan

  • Venue:
  • Neurocomputing
  • Year:
  • 2008

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Abstract

One of the most important aspects of gesture recognition is recognizing hand postures. Much research has been devoted to extracting reliable features for hand posture recognition. In this paper, a new feature alignment approach for hand posture recognition based on curvature scale space (CSS) is presented. The basis point for alignment is based on the two-dimensional distribution of a coordinate-peak set of the CSS image instead of on the coordinate with the maximal peak. A convolution operation is performed with the sequence of a coordinate-peak set and a predefined function. The coordinate with the maximal convolution value is designated as a basis point for aligning the CSS features of the hand posture. Results show that the proposed approach performs well in recognizing hand postures. Furthermore, the proposed approach is more accurate than previous methods based on conventional features.