Abstract
This paper presents a novel method for facial expression recognition based on semi random patches sampling. Different from most of the facial expression methods that use spatio expression descriptor, temporal expression descriptor or both, we extract spatio-temporal expression information by a technology of semi random patches sampling. In the facial feature extraction, expression salient features are first determined; face images are second normalized and warped to a standard face; then temporal expression information is obtained by the differences between the warped expression and its corresponding reference expression model; thirdly, a small set of patches is extracted from both spatio expression information and temporal expression information around each expression salient features. The semi random patches are used to perform facial expression recognition. The proposed semi random patches extraction is simple, yet by leveraging the sparse nature of expression images. Experimental results demonstrate that our approach is able to capture subtle texture variations caused by expression, even under occlusion and corruption.
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Xia, H. (2015). Semi Random Patches Sampling Based on Spatio-temporal Information for Facial Expression Recognition. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_5
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DOI: https://doi.org/10.1007/978-3-319-23989-7_5
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