View-Invariant Gesture Recognition Using Nonparametric Shape Descriptor

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 se...

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Bibliographic Details
Main Authors: Xingyu Wu, Xia Mao, Lijiang Chen, Yuli Xue, Compare, Angelo
Format: Conference Proceeding
Language:English
Subjects:
Feature extraction
Gesture and behavior analysis
Gesture recognition
Human computer interaction
Kernel
Manganese
Motion
Shape
Three-dimensional displays
tracking and video analysis
Trajectory
Online Access:Request full text
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Description
Summary: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.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2014.104