Hand gesture recognition and tracking based on distributed locally linear embedding
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摘要
In this paper, we present a computer vision system for human gesture recognition and tracking based on a new nonlinear dimensionality reduction method. Due to the variation of posture appearance, the recognition and tracking of human hand gestures from one single camera remain a difficult problem. We present an unsupervised learning algorithm, distributed locally linear embedding (DLLE), to discover the intrinsic structure of the data, such as neighborhood relationships information. After the embedding of input images are represented in a lower dimensional space, probabilistic neural network (PNN) is employed and a database is set up for static gesture classification. For dynamic gesture tracking, the similarity among the images sequence are utilized. Hand gesture motion can be tracked and dynamically reconstructed according to the image’s relative position in the corresponding motion database. The method is robust against the input sequence frames and bad image qualities. Experimental results show that our approach is able to successfully separate different hand postures and track the dynamic gesture.
论文关键词:Gesture recognition and tracking,Distributed locally linear embedding (DLLE),Probabilistic neural network (PNN),Hand recognition,Gesture tracking
论文评审过程:Received 8 December 2005, Revised 28 January 2008, Accepted 4 March 2008, Available online 25 March 2008.
论文官网地址:https://doi.org/10.1016/j.imavis.2008.03.004