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A Study on Vision-Based Robust Hand-Posture Recognition by Learning Similarity Between Hand-Posture and Structure

  • Hyoyoung Jang
  • Jin-Woo Jung
  • Zeungnam Bien
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)

Abstract

This paper proposes a robust hand-posture recognition method by learning similarity between hand-posture and structure for the performance improvement of vision-based hand-posture recognition. The difficulties in vision-based hand-posture recognition lie in viewing direction dependency and self-occlusion problem due to the high degree-of-freedom of human hand. General approaches to deal with these problems include multiple camera approach and methods of limiting the relative angle between cameras and the user’s hand. In the case of using multiple cameras, however, fusion techniques to induce the final decision should be considered. Limiting the angle of user’s hand restricts the user’s freedom. The proposed method combines angular features and appearance features to describe hand-postures by a two-layered data structure and includes learning the similarity between the two types of features. The validity of the proposed method is evaluated by applying it to the hand-posture recognition system using three cameras.

Keywords

Feature Vector Gesture Recognition Appearance Feature Feature Layer Automatic Face 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hyoyoung Jang
    • 1
  • Jin-Woo Jung
    • 2
  • Zeungnam Bien
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceKAISTDaejeonKorea
  2. 2.Human-Friendly Welfare Robot System Research CenterKAISTDaejeonKorea

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