Abstract
This paper introduces an automated 3D face pose estimation method using the tetrahedral structure of a nose. This method is based on the feature points extracted from a face surface using curvature descriptors. A nose is the most protruding component in a 3D face image. A nose shape that is composed of the feature points such as a nasion, nose tip, nose base, and nose lobes, and is similar to a tetrahedron. Face pose can be estimated by fitting the tetrahedron to the coordinate axes. Each feature point can be localized by curvature descriptors. This method can be established using nasion, nose tip, and nose base. It can be applied to face tracking and face recognition.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of face: A survey. Proceedings of the IEEE 83(5), 705–740 (1995)
Nikolaidis, A., Pitas, I.: Facial feature extraction and determination of pose. Pattern Recognition 33, 1783–1791 (2000)
Lee, J.C., Milios, E.: Matching range image of human faces. In: Third International Conference on Computer Vision, pp. 722–726 (1990)
Lee, Y.H., Park, K.W., Shim, J.C., Yi, T.H.: 3D Face Recognition using Statistical Multiple Features for the Local Depth Information. In: 16th International Conference on Vision Interface (2003)
Fujiwara: On the detection of feature points of 3D facial image and its application to 3D facial caricature. In: International Conference on 3-D digital Imaging and Modeling (1999)
Gordon, G.: Face Recognition based on depth maps and surface curvature. In: SPIE Geometric methods in Computer Vision, San Diego, vol. 1570 (1991)
Tanaka, H.T., Ikeda, M., Chiaki, H.: Curvature-based face surface recognition using spherical correlation. In: Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 372–377 (1998)
Fromherz, T., Stucki, P., Bichsel, M.: A Survey of Face Recognition, MML Technical Report, No. 97.01, Dept. of Computer Science, University of Zurich (1997)
Beymer, D.: Face Recognition Under Varying Pose. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 756–761 (1994)
Birchfield, S.: Elliptical head tracking using intensity gradients and color histograms. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 232–237 (1998)
Oliver, N., Pentland, A., Berard, F.: Lafter: Lips and face real time tracker. In: Computer Vision and Pattern Recognition (1997)
Ng, J., Gong, S.: Composite support vector machines for detection of faces across views and pose estimation. Image and Vision Computing 20(5-6), 359–368 (2002)
Gee, A.H., Cipolla, R.: Determining the gaze of faces in images. Image and Vision Computing 12(10), 639–647 (1994)
Kruger, N., Potzsch, M., Maurer, T., Rinne, M.: Estimation of face position and pose with labeled graphs. In: British Machine Vision Conference, Edinburgh, pp. 735–743 (1996)
Lanitis, A., Taylor, C.J., Cootes, T.F.: A unified approach to coding and interpreting face images. In: IEEE International Conference on Computer Vision, Cambridge, Massachusetts, pp. 368–373 (1995)
Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, Seattle, pp. 84–91 (1994)
Jebara, T.S.: 3D Pose Estimation and Normalization for Face Recognition. Department of Electrical Engineering. McGill University (1996)
Morency, L.P., Sundberg, P., Darrell, T.: Pose Estimation using 3D View-Based Eigenspaces. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, vol. 45 (2003)
3D Face Scanner, 4DCulture Co., http://www.4dculture.com/
Peet, F.G., Sahota, T.S.: Surface Curvature as a Measure of Image Texture. IEEE Tran. on Pattern Analysis and Machine Intelligence 7(6), 734 (1985)
Hesher, C., Erlebacher, G.: Principal Component Analysis of Range Images for Facial Recognition. In: Proceedings of CISST, Las Vegas (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, ID., Lee, Y., Shim, JC. (2005). An Automated Facial Pose Estimation Using Surface Curvature and Tetrahedral Structure of a Nose. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_35
Download citation
DOI: https://doi.org/10.1007/11558484_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29032-2
Online ISBN: 978-3-540-32046-3
eBook Packages: Computer ScienceComputer Science (R0)