Abstract
A simple and successful scheme for locating the facial features in images at the presence of complex condition context is presented. Multiple fusion steps are taken in cascade. Based on the estimation of the color distribution of the facial features, eye and mouth probability map is constructed using Gaussian Mixture Model (GMM), a fusion strategy on probability maps is then constructs eye, mouth, and skin binary maps. Then the binary fusion is implied to obtain candidate location of each component. Finally, the components are verified by taking facial geometry into consideration. Experiments show that more accurate detection results can be obtained as compared to other state-of-art methods.
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Chen, Y., Ai, C., Hua, C. (2011). Precise Localization of Facial Features Based on Cascade Fusion. In: Chen, R. (eds) Intelligent Computing and Information Science. ICICIS 2011. Communications in Computer and Information Science, vol 134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18129-0_43
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DOI: https://doi.org/10.1007/978-3-642-18129-0_43
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