Hand Modeling and Tracking for Video-Based Sign Language Recognition by Robust Principal Component Analysis

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• 作者：Wei Du (17)
  Justus Piater (17)
  
• 关键词：hand modeling and tracking ; sign language recognition ; robust PCA ; L 1 norm
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：6553
• 期：1
• 页码：286-297
• 全文大小：5897KB
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• 作者单位：Wei Du (17)
  Justus Piater (17)
  
  17. Department of Electrical Engineering and Computer Science, Montefiore Institute, University of Li猫ge, B28, B-4000, Liege, Belgium
  
• ISSN：1611-3349

文摘

Hand modeling and tracking are essential in video-based sign language recognition. The high reformability and the large number of degrees of freedom of hands render the problem difficult. To tackle these challenges, a novel approach based on robust principal component analysis (PCA) is proposed. The robust PCA incorporates an L 1 norm objective function to deal with background clutter, and a projection pursuit strategy to deal with the lack of alignment due to the deformation of hands. The learning algorithm of the robust PCA is very simple, involving only a search for the solutions in a finite set constructed from the training data, which leads to the learning of much more representative and interpretable bases. The incorporation of the L 1 regularization in the fitting of the learned robust PCA models results in cleaner reconstructions and more stable fitting. Based on the robust PCA, a hand tracking system is developed that contains a skin-color region segmentation based on graph cuts and template matching in the framework of particle filtering. Experiments on a publicly available sign-language video database demonstrates the strength of the method.