Face Alignment Using Boosting and Evolutionary Search

Zhang, Hua and Liu, Duanduan and Poel, Mannes and Nijholt, Anton (2010) Face Alignment Using Boosting and Evolutionary Search. In: Ninth Asian Conference on Computer Vision, ACCV 2009. Part II, 23-27 September 2009, Xi'an, China.

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Abstract:	In this paper, we present a face alignment approach using granular features, boosting, and an evolutionary search algorithm. Active Appearance Models (AAM) integrate a shape-texture-combined morphable face model into an efficient fitting strategy, then Boosting Appearance Models (BAM) consider the face alignment problem as a process of maximizing the response from a boosting classifier. Enlightened by AAM and BAM, we present a framework which implements improved boosting classifiers based on more discriminative features and exhaustive search strategies. In this paper, we utilize granular features to replace the conventional rectangular Haar-like features, to improve discriminability, computational efficiency, and a larger search space. At the same time, we adopt the evolutionary search process to solve the deficiency of searching in the large feature space. Finally, we test our approach on a series of challenging data sets, to show the accuracy and efficiency on versatile face images.
Item Type:	Conference or Workshop Item
Copyright:	© 2010 Springer
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Human Media Interaction (HMI)
Link to this item:	http://purl.utwente.nl/publications/71163
Official URL:	http://dx.doi.org/10.1007/978-3-642-12304-7_11
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