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Facial pose estimation using active appearance models and a genric face model
Citation Link: https://doi.org/10.15480/882.3627
Publikationstyp
Conference Paper
Date Issued
2010
Sprache
English
Institut
TORE-DOI
TORE-URI
Start Page
499
End Page
506
Citation
Proceedings of the International Conference on Computer Vision Theory and Applications Vol. 2: 499-506 (2010)
Contribution to Conference
Publisher DOI
Publisher
SCITEPRESS
The complexity in face recognition emerges from the variability of the appearance of human faces. While the identity is preserved, the appearance of a face may change due to factors such as illumination, facial pose or facial expression. Reliable biometric identification relies on an appropriate response to these factors. In this paper we address the estimation of the facial pose as a first step to deal with pose changes. We present a method for pose estimation from two-dimensional images captured under active infrared illumination using a
statistical model of facial appearance. An active appearance model is fitted to the target image to find facial features. We formulate the fitting algorithm using a smooth warp function, namely thin plate splines. The presented algorithm requires only a coarse and generic three-dimensional model of the face to estimate the pose from the detected features locations. The desired field of application requires the algorithm to work with many di erent faces, including faces of subjects not seen during the training stage. A special focus is therefore on the evaluation of the generalization performance of the algorithm which is one weakness of the classic active appearance model algorithm.
statistical model of facial appearance. An active appearance model is fitted to the target image to find facial features. We formulate the fitting algorithm using a smooth warp function, namely thin plate splines. The presented algorithm requires only a coarse and generic three-dimensional model of the face to estimate the pose from the detected features locations. The desired field of application requires the algorithm to work with many di erent faces, including faces of subjects not seen during the training stage. A special focus is therefore on the evaluation of the generalization performance of the algorithm which is one weakness of the classic active appearance model algorithm.
Subjects
Pose estimation
Active appearance model
Infrared imaging
Face recognition
DDC Class
004: Informatik
600: Technik
Funding Organisations
Bundesministerium für Wirtschaft und Technologie
More Funding Information
This work is part of the project KabTec – Modulares integriertes Sicherheitssystem funded by the German Federal Ministry of Economics and Technology.
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