MULTIDIRECTIONAL FACE TRACKING WITH 3D FACE MODEL AND LEARNING HALF-FACE TEMPLATE

Jun’ya Matsuyama, Kuniaki Uehara

Abstract

In this paper, we present an algorithm to detect and track both frontal and side faces in video clips. By means of both learning Haar-Like features of human faces and boosting the learning accuracy with InfoBoost algorithm, our algorithm can detect frontal faces in video clips. We map these Haar-Like features to a 3D model to create the classifier that can detect both frontal and side faces. Since it is costly to detect and track faces using the 3D model, we project Haar-Like features from the 3D model to a 2D space in order to generate various face orientations. By using them, we can detect even side faces in real time without learning frontal faces and side faces separately.

References

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Paper Citation


in Harvard Style

Matsuyama J. and Uehara K. (2006). MULTIDIRECTIONAL FACE TRACKING WITH 3D FACE MODEL AND LEARNING HALF-FACE TEMPLATE . In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 972-8865-40-6, pages 77-84. DOI: 10.5220/0001372700770084


in Bibtex Style

@conference{visapp06,
author={Jun’ya Matsuyama and Kuniaki Uehara},
title={MULTIDIRECTIONAL FACE TRACKING WITH 3D FACE MODEL AND LEARNING HALF-FACE TEMPLATE},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2006},
pages={77-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001372700770084},
isbn={972-8865-40-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - MULTIDIRECTIONAL FACE TRACKING WITH 3D FACE MODEL AND LEARNING HALF-FACE TEMPLATE
SN - 972-8865-40-6
AU - Matsuyama J.
AU - Uehara K.
PY - 2006
SP - 77
EP - 84
DO - 10.5220/0001372700770084