Isolated Sign Language Recognition with Grassmann Covariance Matrices

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  • Hanjie Wang
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
  • Xiujuan Chai
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
  • Xiaopeng Hong
    University of Oulu, Finland
  • Guoying Zhao
    University of Oulu, Finland
  • Xilin Chen
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

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<jats:p> In this article, to utilize long-term dynamics over an isolated sign sequence, we propose a covariance matrix--based representation to naturally fuse information from multimodal sources. To tackle the drawback induced by the commonly used Riemannian metric, the proximity of covariance matrices is measured on the Grassmann manifold. However, the inherent Grassmann metric cannot be directly applied to the covariance matrix. We solve this problem by evaluating and selecting the most significant singular vectors of covariance matrices of sign sequences. The resulting compact representation is called the <jats:italic>Grassmann covariance matrix</jats:italic> . Finally, the Grassmann metric is used to be a kernel for the support vector machine, which enables learning of the signs in a discriminative manner. To validate the proposed method, we collect three challenging sign language datasets, on which comprehensive evaluations show that the proposed method outperforms the state-of-the-art methods both in accuracy and computational cost. </jats:p>

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