Abstract
We present a novel 3D facial feature location method based on the Spin Images registration technique. Three feature points are localized: the nose tip and the inner corners of the right and left eye. The points are found directly in the 3D mesh, allowing a previous normalization before the depth map calculation. This method is applied after a preprocess stage where the candidate points are selected measuring curvatures on the surface and applying clustering techniques. The system is tested on a 3D Face Database called FRAV3D with 105 people and a widely variety of acquisition conditions in order to test the method in a non-controlled environment. The success location rate is 99.5% in the case of the nose tip and 98% in the case of eyes, in frontal conditions. This rate is similar even if the conditions change allowing small rotations. Results in more extremely acquisition conditions are shown too. A complete study of the influence of the mesh resolution over the spin images quality and therefore over the face feature location rate is presented. The causes of the errors are discussed in detail.
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
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Surveys 35(4) (December 2003)
Bowyer, K.W., Chang, K., Flynn, P.: A Survey of 3D and Multi-Modal 3D+2D Face Recognition. In: International Conference on Pattern Recognition (August 2004)
Kittler, J., Hilton, A., Hamouz, M., Illingworth, J.: 3D Assisted Face Recognition: A Survey of 3D imaging, Modelling and Recognition Approaches. In: IEEE CVPR 2005 Workshop on Advanced 3D Imaging for Safety and Security, San Diego, CA (2005)
Johnson, A.E.: Spin-images: A representation for 3-D surface matching. PhD Thesis. Robotics Institute. Carnegie Mellon University (1997)
Ming-Hsuan, Y., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. Pattern Analysis and Machine Intelligence. IEEE Transactions on 24(1), 34–58 (2002)
Colbry, D., Stockman, G., Jain, A.: Detection of Anchor Points for 3D Face Verification. In: Proc. IEEE Workshop on Advanced 3D Imaging for Safety and Security A3DISS, San Diego, CA, June 25 (2005)
Lu, X., Colbry, D., Jain, A.K.: Three dimensional model based face recognition. In: ICPR, Cambridge UK (August 2004)
Irfanoglu, M.O., Gokberk, B., Akarun, L.: 3D shape-based face recognition using automatically registered facial surfaces. Pattern Recognition, 2004. In: ICPR 2004. Proceedings of the 17th International Conference on, August 23-26, 2004, vol. 4, pp. 183–186 (2004)
3DRMA Face Database. http://www.sic.rma.ac.be/~beumier/DB/3drma.html
Gordon, G.: Face recognition based on depth and curvature features. In: CVPR, pp. 108-110 (1992)
Boehnen, C., Russ, T.: A fast multi-modal approach to facial feature detection. In: Workshop on Applications of Computer Vision (2004)
University of Notre Dame Database, http://www.nd.edu/~cvrl/UNDBiometricsDatabase.html
Wang, Y., Chua, C., Ho, Y.: Facial feature detection and face recognition from 2D and 3D images. Pattern Recognition Letters 23(10), 1191–1202 (2002)
Xiao, J., Baker, S., Mathews, I., Kanade, T.: Real-time combined 2D + 3D active appearance models. In: CVPR (June 2004)
Lee, Y., Lee, K., Pan, S.: In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, p. 219. Springer, Heidelberg (2005)
Johnson, A.E., Hebert, M.: Surface matching for object recognition in complex three-dimensional scenes. Image Vision Computing 16, 635–651 (1998)
Johnson, A.E., Hebert, M.: Using Spin Images for efficient object recognition in cluttered 3D scenes. IEEE Trans. PAMI 21(5), 433–449 (1999)
Joachims, T.: Making large-Scale SVM Learning Practical. Advances in Kernel Methods, p. 169
Lyche, T., Schumaker, L.L. (eds.): Mathematical Methods for Curves and Surfaces. Oslo, pp. 135–146. Vanderbilt University Press, Nashville, TN 2000, Copyright 2001, ISBN 0-8265-1378-6
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, ch. 11. Academic Press, London (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Conde, C., Rodríguez-Aragón, L.J., Cabello, E. (2006). Automatic 3D Face Feature Points Extraction with Spin Images. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867661_29
Download citation
DOI: https://doi.org/10.1007/11867661_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44894-5
Online ISBN: 978-3-540-44896-9
eBook Packages: Computer ScienceComputer Science (R0)