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Tzimiropoulos, Georgios; Medina, Joan Alabort; Zafeiriou, Stefanos; Pantic, Maja (2014)
Publisher: IEEE
Languages: English
Types: Article
Subjects:

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
We present Active Orientation Models (AOMs), generative models of facial shape and appearance, which extend the well-known paradigm of Active Appearance Models (AAMs) for the case of generic face alignment under unconstrained conditions. Robustness stems from the fact that the proposed AOMs employ a statistically robust appearance model based on the principal components of image gradient orientations. We show that when incorporated within standard optimization frameworks for AAM learning and fitting, this kernel Principal Component Analysis results in robust algorithms for model fitting. At the same time, the resulting optimization problems maintain the same computational cost. As a result, the main similarity of AOMs with AAMs is the computational complexity. In particular, the project-out version of AOMs is as computationally efficient as the standard project-out inverse compositional algorithm, which is admittedly one of the fastest algorithms for fitting AAMs. We verify experimentally that: 1) AOMs generalize well to unseen variations and 2) outperform all other state-of-the-art AAM methods considered by a large margin. This performance improvement brings AOMs at least in par with other contemporary methods for face alignment. Finally, we provide MATLAB code at http://ibug.doc.ic.ac.uk/resources.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] T.F. Cootes, C.J. Taylor, D.H. Cooper, and J. Graham, “Active shape models-their training and application,” CVIU, vol. 61, no. 1, pp. 38-59, 1995.
    • [2] D. Cristinacce and T. Cootes, “Automatic feature localisation with constrained local models,” Pattern Recognition, vol. 41, no. 10, pp. 3054-3067, 2008.
    • [3] J.M. Saragih, S. Lucey, and J.F. Cohn, “Face alignment through subspace constrained mean-shifts,” in ICCV, 2009.
    • [4] T.F. Cootes, G.J. Edwards, and C.J. Taylor, “Active appearance models,” TPAMI, vol. 23, no. 6, pp. 681-685, 2001.
    • [5] I. Matthews and S. Baker, “Active appearance models revisited,” IJCV, vol. 60, no. 2, pp. 135-164, 2004.
    • [6] R. Gross, I. Matthews, and S. Baker, “Generic vs. person specific active appearance models,” Image and Vision Computing, vol. 23, no. 12, pp. 1080-1093, 2005.
    • [7] G. Tzimiropoulos, S. Zafeiriou, and M. Pantic, “Robust and efficient parametric face alignment,” in ICCV, 2011.
    • [8] G. Tzimiropoulos, S. Zafeiriou, and M. Pantic, “Subspace learning from image gradient orientations,” IEEE TPAMI, vol. 34, no. 12, pp. 2454- 2466, 2012.
    • [9] J.M. Saragih, S. Lucey, and J.F. Cohn, “Deformable model fitting by regularized landmark mean-shift,” IJCV, vol. 91, no. 2, pp. 200-215, 2011.
    • [10] Xuehan Xiong and Fernando De la Torre, “Supervised descent method and its applications to face alignment,” 2013.
    • [11] T.F. Cootes and C.J. Taylor, “On representing edge structure for model matching,” in CVPR, 2001.
    • [12] M. Valstar, B. Martinez, X. Binefa, and M. Pantic, “Facial point detection using boosted regression and graph models,” in CVPR, 2010.
    • [13] X. Zhu, , and D. Ramanan, “Face detection, pose estimation, and landmark estimation in the wild.,” in CVPR, 2012.
    • [14] S. Lucey, Y. Wang, M. Cox, S. Sridharan, and J.F. Cohn, “Efficient constrained local model fitting for non-rigid face alignment,” Image and Vision Computing, vol. 27, no. 12, pp. 1804-1813, 2009.
    • [15] Pedro Martins, Rui Caseiro, Joa˜o F Henriques, and Jorge Batista, “Discriminative bayesian active shape models,” in ECCV. 2012.
    • [16] Vuong Le, Jonathan Brandt, Zhe Lin, Lubomir Bourdev, and Thomas S Huang, “Interactive facial feature localization,” in ECCV. 2012.
    • [17] P.N. Belhumeur, D.W. Jacobs, D.J. Kriegman, and N. Kumar, “Localizing parts of faces using a consensus of exemplars,” in CVPR, 2011.
    • [18] Xudong Cao, Yichen Wei, Fang Wen, and Jian Sun, “Face alignment by explicit shape regression,” in CVPR, 2012.
    • [19] Tim F Cootes, Mircea C Ionita, Claudia Lindner, and Patrick Sauer, “Robust and accurate shape model fitting using random forest regression voting,” in ECCV. 2012.
    • [20] Georgios Tzimiropoulos, Joan Alabort-i Medina, Stefanos Zafeiriou, and Maja Pantic, “Generic active appearance models revisited,” in ACCV 2012, 2013.
    • [21] X. Liu, “Generic face alignment using boosted appearance model,” in CVPR, 2007.
    • [22] H. Wu, X. Liu, and G. Doretto, “Face alignment via boosted ranking model,” in CVPR, 2008.
    • [23] J. Saragih and R. Gocke, “Learning aam fitting through simulation,” Pattern Recognition, vol. 42, no. 11, pp. 2628-2636, 2009.
    • [24] J. Saragih and R. Goecke, “A nonlinear discriminative approach to aam fitting,” in ICCV, 2007.
    • [25] P Kittipanya-ngam and TF Cootes, “The effect of texture representations on aam performance,” in ICPR, 2006.
    • [26] R. Navarathna, S. Sridharan, and S. Lucey, “Fourier active appearance models,” in ICCV, 2011.
    • [27] S. Baker, R. Gross, and I. Matthews, “Lucas-kanade 20 years on: Part 3,” Robotics Institute, Carnegie Mellon University, Tech. Rep. CMU-RITR-03-35, 2003.
    • [28] Bruce D Lucas, Takeo Kanade, et al., “An iterative image registration technique with an application to stereo vision,” in Proceedings of the 7th international joint conference on Artificial intelligence, 1981.
    • [29] Gregory D Hager and Peter N Belhumeur, “Efficient region tracking with parametric models of geometry and illumination,” IEEE TPAMI, vol. 20, no. 10, pp. 1025-1039, 1998.
    • [30] Georgios Tzimiropoulos and Maja Pantic, “Optimization problems for fast aam fitting in-the-wild,” in ICCV, 2013.
    • [31] George Papandreou and Petros Maragos, “Adaptive and constrained algorithms for inverse compositional active appearance model fitting,” in CVPR. IEEE, 2008.
    • [32] F. De La Torre and M.J. Black, “A framework for robust subspace learning,” IJCV, vol. 54, no. 1, pp. 117-142, 2003.
    • [33] Christos Sagonas, Georgios Tzimiropoulos, Stefanos Zafeiriou, and Maja Pantic, “A semi-automatic methodology for facial landmark annotation,” in CVPR-W, 2013.
    • [34] Christos Sagonas, Georgios Tzimiropoulos, Stefanos Zafeiriou, and Maja Pantic, “300 faces in-the-wild challenge: The first facial landmark 10 Maja Pantic (M98, SM06, F12) is Professor in Affective and Behavioural Computing at Imperial College London, Department of Computing, UK, and at the University of Twente, Department of Computer Science, the Netherlands. She received various awards for her work on automatic analysis of human behaviour including the European Research Council Starting Grant Fellowship 2008 and the Roger Needham Award 2011. She currently serves as the Editor in Chief of Image and Vision Computing Journal and as an Associate Editor for both the IEEE Trans. Pattern Analysis and Machine Intelligence and the IEEE Trans.
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