Abstract
We propose an automatic method for person-independent fitting of a deformable 3D face mask model under varying illumination conditions. Principle Component Analysis is utilised to build a face model which is then used within a particle filter based approach to fit the mask to the image. By subdividing a coarse mask and using a novel texture mapping technique, we further apply the 3D face model to fit into lower resolution images. The illumination invariance is achieved by representing each face as a combination of harmonic images within the weighting function of the particle filter. We demonstrate the performance of our approach on the IMM Face Database and the Extended Yale Face Database B and show that it outperforms the Active Shape Models approach [6].
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References
Ahlberg, J.: CANDIDE-3 - an updated parameterized face. Technical Report LiTH-ISY-R-2326, Dept. of Elec. Eng., Linkoping University, Sweden (2001)
Ayala Raggi, S., Altamirano Robles, L., Cruz Enriquez, J.: Towards an illumination-based 3D active appearance model for fast face alignment. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. LNCS, vol. 5197, pp. 568–575. Springer, Heidelberg (2008)
Basri, R., Jacobs, D.W.: Lambertian reflectance and linear subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 218–233 (2003)
Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1063–1074 (2003)
Cai, D.: Codes and datasets for subspace learning (2007), http://www.cs.uiuc.edu/homes/dengcai2/Data/data.html (retrieved, October 2007)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models—their training and application. Comp. Vis. Image Underst. 61(1), 38–59 (1995)
Dedeoglu, G., Baker, S., Kanade, T.: Resolution-aware fitting of active appearance models to low-resolution images. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 83–97. Springer, Heidelberg (2006)
Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. International Journal of Computer Vision 61(2), 185–205 (2005)
Dornaika, F., Ahlberg, J.: Fitting 3D face models for tracking and Active Appearance Model training. Image and Vision Computing 24(9), 1010–1024 (2006)
Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10(3), 197–208 (2000)
Edwards, G.J., Taylor, C.J., Cootes, T.F.: Interpreting face images using active appearance models. In: Face & Gesture Recognition, p. 300. IEEE, Los Alamitos (1998)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001)
Hamarneh, G.: Multi-resolution active shape models (2008), http://www.cs.sfu.ca/~hamarneh/software/asm/index.html (retrieved, February 2008)
Kalinkina, D., Gagalowicz, A., Roussel, R.: 3d reconstruction of a human face from images using morphological adaptation. In: Gagalowicz, A., Philips, W. (eds.) MIRAGE 2007. LNCS, vol. 4418, pp. 212–224. Springer, Heidelberg (2007)
Kuhl, A., Tan, T., Venkatesh, S.: Model-based combined tracking and resolution enhancement. In: Intl. Conf. on Pattern Recognition. IEEE, Los Alamitos (2008)
Lu, L., Zhang, Z., Shum, H.-Y., Liu, Z., Chen, H.: Model- and exemplar-based robust head pose tracking under occlusion and varying expression. In: IEEE Workshop on Models versus Exemplars in Computer Vision (2001)
Nordstrøm, M.M., Larsen, M., Sierakowski, J., Stegmann, M.B.: The IMM face database - an annotated dataset of 240 face images. Technical report, Technical University of Denmark, DTU (May 2004)
Tang, H., Huang, T.S.: MPEG4 performance-driven avatar via robust facial motion tracking. In: IEEE International Conference on Image Processing (2008)
Tu, J., Zhang, Z., Zeng, Z., Huang, T.: Face localization via hierarchical condensation with fisher boosting feature selection. In: CVPR, pp. II: 719–724 (2004)
Turk, M., Pentland, A.: Face recognition using eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 586–591 (1991)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition (2001)
Xiao, J., Baker, S., Matthews, I., Kanade, T.: Real-time combined 2D+3D active appearance models. In: IEEE Comp. Vis. and Pattern Recog., pp. 535–542 (2004)
Xin, L., Wang, Q., Tao, J., Tang, X., Tan, T., Shum, H.: Automatic 3D face modeling from video. In: International Conference on Computer Vision, October 2005, vol. 2, pp. 1193–1199. IEEE, Los Alamitos (2005)
Yan, S., Li, M., Zhang, H., Cheng, Q.: Ranking prior likelihood distributions for bayesian shape localization framework. In: Intl. Conf. on Comp. Vis. IEEE, Los Alamitos (2003)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)
Zorin, D., Schroder, P.: Subdivision for modeling and animation. In: Subdivision for modeling and animation, Computer Graphics and Interactive Techniques. ACM Siggraph, vol. 36 (July 2000)
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Kuhl, A., Tan, T., Venkatesh, S. (2009). Automatic Fitting of a Deformable Face Mask Using a Single Image. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics CollaborationTechniques. MIRAGE 2009. Lecture Notes in Computer Science, vol 5496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01811-4_7
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DOI: https://doi.org/10.1007/978-3-642-01811-4_7
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