Abstract
Curvature Gabor features have recently been shown to be powerful facial texture descriptors with applications on face recognition. In this paper we introduce their use in facial action unit (AU) detection within a novel framework that combines multiple Local Curvature Gabor Binary Patterns (LCGBP) on different filter sizes and curvature degrees. The proposed system uses the distances of LCGBP histograms between neutral faces and AU containing faces combined with an AU-specific feature selection and classification process. We achieve 98.6% overall accuracy in our tests with the extended Cohn-Kanade database, which is higher than achieved previously by any state-of-the-art method.
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Yüce, A., Arar, N.M., Thiran, JP. (2013). Multiple Local Curvature Gabor Binary Patterns for Facial Action Recognition. In: Salah, A.A., Hung, H., Aran, O., Gunes, H. (eds) Human Behavior Understanding. HBU 2013. Lecture Notes in Computer Science, vol 8212. Springer, Cham. https://doi.org/10.1007/978-3-319-02714-2_12
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DOI: https://doi.org/10.1007/978-3-319-02714-2_12
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