Hand Gesture Recognition and Tracking based on Distributed Locally Linear Embedding

Due to the variations of posture appearance, the recognition and tracking of human hand gesture remain a difficult problem. In this paper, an unsupervised learning algorithm, distributed locally linear embedding (DLLE), is proposed to discover the intrinsic structure of the data, such as neighborhood relationships and global distributions. These discovered properties are used to compute their corresponding low-dimensional embedding, and then probabilistic neural network (PNN) is employed and a database is set up for static gesture classification. For dynamic gesture tracking, we make use of the similarity measures among the images, hand gesture motion from a sequence of images can be tracked and dynamically reconstructed according to the image's relative position in the corresponding motion database. Experimental results show that our approach is able to successfully separate different hand postures and track the dynamic gesture

[1]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[2]  George Kollios,et al.  BoostMap: A method for efficient approximate similarity rankings , 2004, CVPR 2004.

[3]  Aditya Ramamoorthy,et al.  Recognition of dynamic hand gestures , 2003, Pattern Recognit..

[4]  Ying Wu,et al.  Vision-Based Gesture Recognition: A Review , 1999, Gesture Workshop.

[5]  Alex Pentland,et al.  Task-Specific Gesture Analysis in Real-Time Using Interpolated Views , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[7]  Surendra Ranganath,et al.  Real-time gesture recognition system and application , 2002, Image Vis. Comput..

[8]  Takeo Kanade,et al.  Visual Tracking of High DOF Articulated Structures: an Application to Human Hand Tracking , 1994, ECCV.

[9]  David Zeltzer,et al.  A survey of glove-based input , 1994, IEEE Computer Graphics and Applications.

[10]  Shuzhi Sam Ge,et al.  Face recognition by applying wavelet subband representation and kernel associative memory , 2004, IEEE Transactions on Neural Networks.

[11]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[12]  Neff Walker,et al.  Evaluation of the CyberGlove as a whole-hand input device , 1995, TCHI.

[13]  Shuzhi Sam Ge,et al.  Feature representation based on intrinsic structure discovery in high dimensional space , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[14]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[15]  Pooja Mittal,et al.  Developing a Gesture-based Interface , 2002 .

[16]  Thomas S. Huang,et al.  Vision based hand modeling and tracking for virtual teleconferencing and telecollaboration , 1995, Proceedings of IEEE International Conference on Computer Vision.

[17]  Geoffrey E. Hinton,et al.  Glove-TalkII: an adaptive gesture-to-formant interface , 1995, CHI '95.

[18]  Michael J. Black,et al.  EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, International Journal of Computer Vision.

[19]  T. Cacoullos.  Estimation of a multivariate density , 1966 .

[20]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[21]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[22]  Bayya Yegnanarayana,et al.  Supervised texture classification using a probabilistic neural network and constraint satisfaction model , 1998, IEEE Trans. Neural Networks.

[23]  M. Naderi.  Think globally... , 2004, HIV prevention plus!.

[24]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

[25]  Ying Wu,et al.  Modeling the constraints of human hand motion , 2000, Proceedings Workshop on Human Motion.

[26]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[27]  Haiying Guan,et al.  Model-based 3D hand posture estimation from a single 2D image , 2002, Image Vis. Comput..

[28]  Tosiyasu L. Kunii,et al.  Model-based analysis of hand posture , 1995, IEEE Computer Graphics and Applications.

[29]  Michael Girard,et al.  Computer animation of knowledge-based human grasping , 1991, SIGGRAPH.

[30]  Vladimir Pavlovic,et al.  Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Tosiyasu L. Kunii,et al.  Constraint-Based Hand Animation , 1993 .