Abstract
3D face landmarking aims at automatic localization of 3D facial features and has a wide range of applications, including face recognition, face tracking, facial expression analysis. Methods so far developed for 2D images were shown sensitive to lighting condition changes. In this paper, we propose a learning-based approach for reliable locating of face landmarks in 3D. Our approach relies on a statistical model, called 3D Statistical Facial feAture Model(SFAM) in the paper, which learns both global variations in 3D face morphology and local ones around the 3D face landmarks in terms of local texture and shape. Experimented on FRGC v1.0 dataset, our approach shows its effectiveness and achieves 99.09% of locating accuracy in 10mm precision. The mean error and standard deviation of each landmark are respectively less than 5mm and 4.
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References
Ali Salah, A., Cinar, H., Akarun, L., Sankur, B.: Robust Facial Landmarking for Registration. Annals of Telecommunications 62(1-2), 1608–1633 (2007)
Bowyer, K.W., Chang, K., Flynn, P.: A Survey of Approaches and Challenges in 3D and Multi-Modal 3D+2D Face Recognition. Journal of Computer Vision and Image Understanding 101(1), 1–15 (2006)
Conde, C., Serrano, A.: 3D Facial Normalization with Spin Images and Influence of Range Data Calculation over Face Verification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 20–26 (2005)
Xu, C., Tan, T., Wang, Y., Quan, L.: Combining Local Features for Robust Nose Location in 3D Facial Data. Pattern Recognition Letters 27(13), 1487–1494 (2006)
Bevilacqua, V., Casorio, P., Mastronardi, G.: Extending Hough Transform to a Points Cloud for 3D-Face Nose-Tip Detection. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS (LNAI), vol. 5227, pp. 1200–1209. Springer, Heidelberg (2008)
Huertas, M.R., Pears, N.: 3D Facial Landmark Localisation by Matching Simple Descriptors. In: Proceedings of the International Conference on Biometrics: Theory, Applications and Systems, pp. 1–6 (2008)
Chang, K.I., Bowyer, K.W., Flynn, P.J.: Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1695–1700 (2006)
D’House, J., Colineau, J., Bichon, C., Dorizzi, B.: Precise localization of landmarks on 3d faces using gabor wavelets. In: Proceedings of the International Conference on Biometrices: Theory, Applications, and Systems, pp. 1–6 (2007)
Faltemier, T.C., Bowyer, K.W., Flynn, P.J.: Rotated profile signatures for robust 3d feature detection. In: Proceedings of the International Conference on Face and Gesture Recognition (2008)
Xua, C., Tana, T., Wang, Y., Quanc, L.: Combining local features for robust nose location in 3D facial data. Pattern Recognition Letters 27(13), 1487–1494 (2006)
Colbry, D., Stockman, G., Anil, J.: Detection of Anchor Points for 3D Face Verification. In: Proceedings of Computer Vision and Pattern Recognition - Workshops, p. 118 (2005)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Transaction on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)
Cristinacce, D., Cootes, T.F.: Automatic Feature Localisation with Constrained Local Models. Journal of Pattern Recognition 41(10), 3054–3067 (2008)
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Proceedings of the 26th Annual Conference on Computer Graphics and interactive Techniques, pp. 187–194 (1999)
Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7, 308–313 (1965)
Soille, P.: Morphological Image Analysis: Principles and Applications, pp. 173–174. Springer, Heidelberg (1999)
Jahanbin, S., Bovik, A.C., Choi, H.: Automated facial feature detection from portrait and range images. In: Proceedings of IEEE Southwest Symposium on Image Analysis and Interpretation (2008)
Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision 13(2), 119–152 (1994)
Dibeklioglu, H., Salah, A.A., Akarun, L.: 3D Facial Landmarking under Expression, Pose, and Occlusion Variations. In: Proceedings of the International Conference on Biometrics: Theory, Applications and Systems, pp. 1–6 (2008)
Lu, X., Jain, A.K.: Automatic feature extraction for multiview 3D face recognition. In: Proceedings of the Internationl Conference on Face and Gesture Recognition, pp. 585–590 (2006)
Szeptycki, P., Ardabilian, M., Chen, L.: Automatic and precise 2.5D face landmarking. Technical Report, LIRIS (2009)
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 947–954 (2005)
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Zhao, X., Dellandréa, E., Chen, L. (2009). A 3D Statistical Facial Feature Model and Its Application on Locating Facial Landmarks. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04697-1_64
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DOI: https://doi.org/10.1007/978-3-642-04697-1_64
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