@inproceedings{9f11d8c1fef04bb9932db7dace262296,
title = "Continuous hand gesture recognition in the learned hierarchical latent variable space",
abstract = "We describe a hierarchical approach for recognizing continuous hand gestures. It consists of hierarchical nonlinear dimensionality reduction based feature extraction and Hierarchical Conditional Random Field (Hierarchical CRF) based motion modeling. Articulated hands can be decomposed into several hand parts and we explore the underlying structures of articulated action spaces for both the hand and hand parts using Hierarchical Gaussian Process Latent Variable Model (HGPLVM). In this hierarchical latent variable space, we propose a Hierarchical CRF, which can simultaneously capture the extrinsic class dynamics and learn the relationship between motions of hand parts and class labels, to model the hand motions. Approving recognition performance is obtained on our user-defined hand gesture dataset.",
keywords = "Crf, Dimensionality reduction, Gesture recognition, Hierarchical",
author = "Lei Han and Wei Liang",
year = "2008",
doi = "10.1007/978-3-540-70517-8\_4",
language = "English",
isbn = "3540705163",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "32--41",
booktitle = "Articulated Motion and Deformable Objects - 5th International Conference, AMDO 2008, Proceedings",
note = "5th International Conference on Articulated Motion and Deformable Objects, AMDO 2008 ; Conference date: 09-07-2008 Through 11-07-2008",
}