Gesture Recognition Based on Manifold Learning Conference Paper

abstract

• Current feature-based gesture recognition systems use human-chosen features to perform recognition. Effective features for classification can also be automatically learned and chosen by the computer. In other recognition domains, such as face recognition, manifold learning methods have been found to be good nonlinear feature extractors. Few manifold learning algorithms, however, have been applied to gesture recognition. Current manifold learning techniques focus only on spatial information, making them undesirable for use in the domain of gesture recognition where stroke timing data can provide helpful insight into the recognition of hand-drawn symbols. In this paper, we develop a new algorithm for multi-stroke gesture recognition, which integrates timing data into a manifold learning algorithm based on a kernel Isomap. Experimental results show it to perform better than traditional human-chosen feature-based systems. 2008 Springer Berlin Heidelberg.

authors

• Hammond, Tracy

published proceedings

• STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION

author list (cited authors)

• Choi, H., Paulson, B., & Hammond, T.

citation count

• 4

complete list of authors

• Choi, Heeyoul||Paulson, Brandon||Hammond, Tracy

publication date

• December 2008

publisher

• Springer Nature  Publisher

published in

• Lecture Notes in Artificial Intelligence  Journal

keywords

• Kernel Isomap
• Manifold Learning
• Sketch Recognition

Digital Object Identifier (DOI)

• 10.1007/978-3-540-89689-0_29

International Standard Book Number (ISBN) 10

• 3540896880

International Standard Book Number (ISBN) 13

• 978-3-540-89688-3

start page

• 247

end page

• 256

volume

• 5342

URL

• http://dx.doi.org/10.1007/978-3-540-89689-0_29