Manifold Learning for Hand Pose Recognition: Evaluation Framework

Conference paper

DOI: 10.1007/978-3-319-34099-9_55

Part of the Communications in Computer and Information Science book series (CCIS, volume 613)
Cite this paper as:
Papiez M., Kawulok M., Nalepa J. (2016) Manifold Learning for Hand Pose Recognition: Evaluation Framework. In: Kozielski S., Mrozek D., Kasprowski P., Małysiak-Mrozek B., Kostrzewa D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS 2015, BDAS 2016. Communications in Computer and Information Science, vol 613. Springer, Cham

Abstract

Hand pose recognition from 2D still images is an important, yet very challenging problem of data analysis and pattern recognition. Among many approaches proposed, there have been some attempts to exploit manifold learning for recovering intrinsic hand pose features from the hand appearance. Although they were reported successful in solving particular problems related with recognizing a hand pose, there is a lack of a thorough study on how well these methods discover the intrinsic hand dimensionality. In this study, we introduce an evaluation framework to assess several state-of-the-art methods for manifold learning and we report the results obtained for a set of artificial images generated from a hand model. This will help in future deployments of manifold learning to hand pose estimation, but also to other multidimensional problems common to the big data scenarios.

Keywords

Hand pose estimation Gesture recognition Manifold learning 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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