Model-less 3D Head Pose Estimation using Self-optimized Local Discriminant Embedding

F. Dornaika, A. Bosgahzadeh, A. Assoum

2013

Abstract

In this paper, we propose a self-optimized Local Discriminant Embedding and apply it to the problem of model-less 3D head pose estimation. Recently, Local Discriminant Embedding (LDE) method was proposed in order to tackle some limitations of the global Linear Discriminant Analysis (LDA) method. In order to better characterize the discriminant property of the data, LDE builds two adjacency graphs: the within-class adjacency graph and the between-class adjacency graph. However, it is very difficult to set in advance these two graphs. Our proposed self-optimized LDE has two important characteristics: (i) while all graph-based manifold learning techniques (supervised and unsupervised) are depending on several parameters that require manual tuning, ours is parameter-free, and (ii) it adaptively estimates the local neighborhood surrounding each sample based on the data similarity. The resulting self-optimized LDE approach has been applied to the problem of model-less coarse 3D head pose estimation (person independent 3D pose estimation). It was tested on two large databases: FacePix and Pointing’04. It was conveniently compared with other linear techniques. The experimental results confirm that our method outperforms, in general, the existing ones.

Paper Citation

in Harvard Style

Dornaika F., Bosgahzadeh A. and Assoum A. (2013). Model-less 3D Head Pose Estimation using Self-optimized Local Discriminant Embedding . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 347-352. DOI: 10.5220/0004347503470352

in Bibtex Style

@conference{visapp13,
author={F. Dornaika and A. Bosgahzadeh and A. Assoum},
title={Model-less 3D Head Pose Estimation using Self-optimized Local Discriminant Embedding},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={347-352},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004347503470352},
isbn={978-989-8565-48-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - Model-less 3D Head Pose Estimation using Self-optimized Local Discriminant Embedding
SN - 978-989-8565-48-8
AU - Dornaika F.
AU - Bosgahzadeh A.
AU - Assoum A.
PY - 2013
SP - 347
EP - 352
DO - 10.5220/0004347503470352