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Authors: M. Saquib Sarfraz and Olaf Hellwich

Affiliation: Computer vision and Remote Sensing, Berlin university of Technology, Germany

ISBN: 978-989-8111-25-8

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Feature Extraction ; Features Extraction ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Informatics in Control, Automation and Robotics ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing ; Statistical Approach

Abstract: Recognizing a face from a novel view point poses major challenges for automatic face recognition. Recent methods address this problem by trying to model the subject specific appearance change across pose. For this, however, almost all of the existing methods require a perfect alignment between a gallery and a probe image. In this paper we present a pose invariant face recognition method centered on modeling joint appearance of gallery and probe images across pose in a probabilistic framework. We propose novel extensions in this direction by introducing to use a more robust feature description as opposed to pixel-based appearances. Using such features we put forward to synthesize the non-frontal views to frontal. Furthermore, using local kernel density estimation, instead of commonly used normal density assumption, is suggested to derive the prior models. Our method does not require any strict alignment between gallery and probe images which makes it particularly attractive as compared to the existing state of the art methods. Improved recognition across a wide range of poses has been achieved using these extensions. (More)

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Paper citation in several formats:
Saquib Sarfraz M.; Hellwich O. and (2008). Learning Probabilistic Models for Recognizing Faces under Pose Variations.In Proceedings of the 1st International Workshop on Image Mining Theory and Applications IMTA 2008 - Volume 1: IMTA, (VISIGRAPP 2008) ISBN 978-989-8111-25-8, pages 122-132. DOI: 10.5220/0002341001220132

@conference{imta08,
author={M. {Saquib Sarfraz} and Olaf Hellwich},
title={Learning Probabilistic Models for Recognizing Faces under Pose Variations},
booktitle={Proceedings of the 1st International Workshop on Image Mining Theory and Applications IMTA 2008 - Volume 1: IMTA, (VISIGRAPP 2008)},
year={2008},
pages={122-132},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002341001220132},
isbn={978-989-8111-25-8},
}

TY - CONF

JO - Proceedings of the 1st International Workshop on Image Mining Theory and Applications IMTA 2008 - Volume 1: IMTA, (VISIGRAPP 2008)
TI - Learning Probabilistic Models for Recognizing Faces under Pose Variations
SN - 978-989-8111-25-8
AU - Saquib Sarfraz, M.
AU - Hellwich, O.
PY - 2008
SP - 122
EP - 132
DO - 10.5220/0002341001220132

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