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Authors: Jun’ya Matsuyama and Kuniaki Uehara

Affiliation: Graduate School of Science & Technology, Kobe University, Japan

ISBN: 972-8865-40-6

ISSN: 2184-4321

Keyword(s): Face Tracking, Face Detection, AdaBoost, InfoBoost, 3D-Model, Half-Face, Cascading, Random Sampling.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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

@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},
}

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

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