Abstract
This work presents a simple and very efficient solution to align facial parts in unseen images. Our solution relies on a Point Distribution Model (PDM) face model and a set of discriminant local detectors, one for each facial landmark. The patch responses can be embedded into a Bayesian inference problem, where the posterior distribution of the global warp is inferred in a ımaximum a posteriori (MAP) sense. However, previous formulations do not model explicitly the covariance of the latent variables, which represents the confidence in the current solution. In our Discriminative Bayesian Active Shape Model (DBASM) formulation, the MAP global alignment is inferred by a Linear Dynamical System (LDS) that takes this information into account. The Bayesian paradigm provides an effective fitting strategy, since it combines in the same framework both the shape prior and multiple sets of patch alignment classifiers to further improve the accuracy. Extensive evaluations were performed on several datasets including the challenging Labeled Faces in the Wild (LFW). Face parts descriptors were also evaluated, including the recently proposed Minimum Output Sum of Squared Error (MOSSE) filter. The proposed Bayesian optimization strategy improves on the state-of-the-art while using the same local detectors. We also show that MOSSE filters further improve on these results.
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Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE TPAMI 23, 681–685 (2001)
Matthews, I., Baker, S.: Active appearance models revisited. IJCV 60, 135–164 (2004)
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst (2007)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. CVIU 61, 38–59 (1995)
Cristinacce, D., Cootes, T.F.: Boosted regression active shape models. In: BMVC (2007)
Tresadern, P., Bhaskar, H., Adeshina, S., Taylor, C., Cootes, T.F.: Combining local and global shape models for deformable object matching. In: BMVC (2009)
Cristinacce, D., Cootes, T.F.: Automatic feature localisation with constrained local models. Pattern Recognition 41, 3054–3067 (2008)
Wang, Y., Lucey, S., Cohn, J.: Enforcing convexity for improved alignment with constrained local models. In: IEEE CVPR (2008)
Gu, L., Kanade, T.: A Generative Shape Regularization Model for Robust Face Alignment. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 413–426. Springer, Heidelberg (2008)
Saragih, J., Lucey, S., Cohn, J.: Face alignment through subspace constrained mean-shifts. In: IEEE ICCV (2009)
Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: IEEE CVPR (2010)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)
Paquet, U.: Convexity and bayesian constrained local models. In: IEEE CVPR (2009)
Shen, C., Brooks, M.J., Hengel, A.: Fast global kernel density mode seeking: Applications to localization and tracking. IEEE TIP 16, 1457–1469 (2007)
Nordstrom, M., Larsen, M., Sierakowski, J., Stegmann, M.: The IMM face database - an annotated dataset of 240 face images. Technical report, Technical University of Denmark, DTU (2004)
Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust Face Detection Using the Hausdorff Distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)
Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: XM2VTSDB: The extended M2VTS database. In: AVBPA (1999)
FGNet: Talking face video (2004)
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A library for large linear classification. JMLR, 1871–1874 (2008)
Cristinacce, D., Cootes, T.F.: Facial feature detection using adaboost with shape constraints. In: BMVC (2003)
Cristinacce, D., Cootes, T.F.: Feature detection and tracking with constrained local models. In: BMVC (2006)
Viola, P., Jones, M.: Robust real-time object detection. IJCV 57, 137–154 (2002)
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Martins, P., Caseiro, R., Henriques, J.F., Batista, J. (2012). Discriminative Bayesian Active Shape Models. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33712-3_5
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DOI: https://doi.org/10.1007/978-3-642-33712-3_5
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