Abstract
At present, many approaches have been proposed for deformable face alignment with varying degrees of success. However, the common drawback to nearly all these approaches is the inaccurate landmark registrations. The registration errors which occur are predominantly heterogeneous (i.e. low error for some frames in a sequence and higher error for others). In this paper we propose an approach for simultaneously aligning an ensemble of deformable face images stemming from the same subject given noisy heterogeneous landmark estimates. We propose that these initial noisy landmark estimates can be used as an “anchor” in conjunction with known state-of-the-art objectives for unsupervised image ensemble alignment. Impressive alignment performance is obtained using well known deformable face fitting algorithms as “anchors”.
Chapter PDF
Similar content being viewed by others
References
Matthews, I., Baker, S.: Active appearance models revisited. International Journal of Computer Vision 60, 135–164 (2004)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)
Saragih, J.M., Lucey, S., Cohn, J.: Face alignment through subspace constrained mean-shifts. In: International Conference of Computer Vision, ICCV (2009)
Learned-Miller, E.G.: Data driven image models through continuous joint alignment. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 236–250 (2006)
Cox, M., Lucey, S., Sridharan, S., Cohn, J.: Least squares congealing for unsupervised alignment of images. In: IEEE International Conference on Computer Vision and Pattern Recognition, CVPR (2008)
Peng, Y., Ganesh, A., Wright, J., Xu, W., Ma, Y.: Rasl: Robust alignment by sparse and low-rank decomposition for linearly correlated images. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 763–770 (2010)
Wright, J., Ma, Y., Ganesh, A., Rao, S.: Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization. In: Proceedings of Neural Information Processing Systems, NIPS (2009)
Baker, S., Matthews, I.: Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision 56, 221–255 (2004)
Gross, R., Matthews, I., Cohn, J.F., Kanade, T., Baker, S.: Multi-PIE. Image and Vision Computing (2009)
Saraghi, J.: Facetracker (2011), http://web.mac.com/jsaragih/FaceTracker/FaceTracker.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheng, X., Sridharan, S., Saraghi, J., Lucey, S. (2012). Anchored Deformable Face Ensemble Alignment. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33863-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-33863-2_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33862-5
Online ISBN: 978-3-642-33863-2
eBook Packages: Computer ScienceComputer Science (R0)