Face Modeling and Tracking

Summary

Active Appearance Models (AAMs) represent facial images with generative models for both shape and appearance of the face. Despite their success, they enjoy limited performance when used on faces that were not part of the training set. Moreover, training them with a lot of examples degrades their effectiveness. This limits their applicability for tracking multiple unseen faces in unconstrained scenarios. In (Wu et al., 2008) these issues are addressed by learning a discriminative face model which is fitted by minimizing a cost function that is concave. It turns out that this is equivalent to learning a ranking function. The framework shows a dramatic improvement over AAMs in terms of alignment robustness and speed, enabling the simultaneous real-time tracking of tens of faces. The approach describes a general methodology applicable to the many problems (e.g. discriminative object tracking) that can be solved by learning the cost function to be optimized, and which it has been shown it can be imposed to be either concave or convex.

References

1. CVPR

   Face alignment using boosted ranking models Wu, H., Liu, X., and Doretto, G. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2008. Oral abstract bibTeX pdf

   Face alignment seeks to deform a face model to match it with the features of the image of a face by optimizing an appropriate cost function. We propose a new face model that is aligned by maximizing a score function, which we learn from training data, and that we impose to be concave. We show that this problem can be reduced to learning a classifier that is able to say whether or not by switching from one alignment to a new one, the model is approaching the correct fitting. This relates to the ranking problem where a number of instances need to be ordered. For training the model, we propose to extend GentleBoost [23] to ranklearning. Extensive experimentation shows the superiority of this approach to other learning paradigms, and demonstrates that this model exceeds the alignment performance of the state-of-the-art.

   @inproceedings{wuLD08cvpr,
     abbr = {CVPR},
     author = {Wu, H. and Liu, X. and Doretto, G.},
     title = {Face alignment using boosted ranking models},
     booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision
     	and Pattern Recognition},
     year = {2008},
     pages = {1--8},
     bib2html_pubtype = {Conferences},
     bib2html_rescat = {Video Analysis, Appearance Modeling, Shape and Appearance Modeling,
     	Integral Image Computations, Face Tracking, Face Modeling},
     file = {wuLD08cvpr.pdf:doretto\\conference\\wuLD08cvpr.pdf:PDF;wuLD08cvpr.pdf:doretto\\conference\\wuLD08cvpr.pdf:PDF},
     owner = {doretto},
     timestamp = {2008.01.16},
     wwwnote = {Oral}
   }