In this paper we propose a new method for view-invariant gesture recognition, based on what we call nonparametric shape descriptor. We represent gestures as 3D motion trajectories and then we prove that the shape of a trajectory is equivalent to the Euclidean distances between all its points. The set of point-to-point distances description is mapped to a high-dimensional kernel space by kernel principal component analysis (KPCA), and then nonparametric discriminant analysis (NDA) is used to extract the view-invariant shape features as the input for pattern classification. The algorithm is performed on a public dataset, and shows better view-invariant performance than other state-of-the-art methods.
['Xingyu Wu', 'Xia Mao', 'Lijiang Chen', 'Yuli Xue', 'Angelo Compare']
View-Invariant Gesture Recognition Using Nonparametric Shape Descriptor