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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Face Alignment Using Boosting and Evolutionary Search
verfasst von : Hua Zhang, Duanduan Liu, Mannes Poel, Anton Nijholt
Erschienen in: Computer Vision – ACCV 2009
Verlag: Springer Berlin Heidelberg
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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.