Abstract
Abrupt performance degradation caused by face pose variations has been one of the bottlenecks for practical face recognition applications. This paper presents a practical pose normalization technique by using a generic 3D face model as a priori. The 3D face model greatly facilitates the setup of the correspondence between non-frontal and frontal face images, which can be exploited as a priori to transform a non-frontal face image, with known pose but very sparse correspondence with the generic face model, into a frontal one by warping techniques. Our experiments have shown that the proposed method can greatly improve the recognition performance of the current face recognition methods without pose normalization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brunelli, R., Poggio, T.: Face Recognition: Features versus Template. TPAMI 15(10), 1042–1052 (1993)
Pentland, A., Moghaddam, B., Starner, T.: View-based and Modular Eigenspace for Face Recognition. In: IEEE CVPR, pp. 84–91 (1994)
Murase, H., Nayar, S.K.: Visual Learning and Recognition of 3-D Objects form Appearance. International Journal of Computer Vision 14, 5–24 (1995)
Murase, H., Nayar, S.: Learning and Recognition of 3D Objects from Appearance. International Journal of Computer Vision, 5–25 (January 1995)
SmMcKenna, S.G., Collins, J.J.: Face Tracking and Pose Representation. In: British Machine Vision Conference, Edinburgh, Scotland (1996)
Valentin, D., Abdi, H.: Can a Linear Autoassociator Recognize Faces From New Orientations. Journal of the Optical Society of American A-optics, Image Science and Vision 13(4), 717–724 (1996)
Blanz, V., Vetter, T.: A Morphable Model for the Synthesis of 3D Faces. Proc. SIGGRAPH, 187–194 (1999)
Cootes, T.F., Walker, K., Taylor, C.J.: View-Based Active Appearance Models. In: IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, p. 227 (March 2000)
Zhou, Z., HuangFu, J., Zhang, H., Chen, Z.: Neural Network Ensemble Based View Invariant Face Recognition. Journal of Computer Study and Development 38(9), 1061–1065 (2001)
Gross, R., Matthews, I., Baker, S.: Eigen Light-Fields and Face Recognition Across Pose. In: Proceedings of the Fifth International Conference on Face and Gesture Recognition (2002)
Gross, R., Matthews, I., Baker, S.: Appearance-Based Face Recognition and Light Fields, Tech. Report CMU-RI-TR-02-20, Robotics Institute, Carnegie Mellon University (August. 2002)
Jiang, D.L., Gao, W., Wang, Z.Q., Chen, Y.Q.: Realistic 3D Facial Animation with Subtle Texture Changes. In: ICICS-PCM2003, Singapore (December 2003)
Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE Transactions on PAMI 25, 1063–1074 (2003)
Gao, W., Cao, B., Shan, S.G., Zhou, D.L., Zhang, X.H., Zhao, D.B.: The CAS-PEAL Large-Scale Chinese Face DataBase and Evaluation Protocols, Technique Report No. JDL-TR_04_FR_001, Joint Research & Development Laboratory, CAS (2004), http://www.jdl.ac.cn
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chai, X., Shan, S., Gao, W., Liu, X. (2004). Pose Normalization Using Generic 3D Face Model as a Priori for Pose-Insensitive Face Recognition. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds) Advances in Biometric Person Authentication. SINOBIOMETRICS 2004. Lecture Notes in Computer Science, vol 3338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30548-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-30548-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24029-7
Online ISBN: 978-3-540-30548-4
eBook Packages: Computer ScienceComputer Science (R0)