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- W2126234799 abstract "We present a regression-based scheme for multi-view facial expression recognition based on 2D geometric features. We address the problem by mapping facial points (e.g. mouth corners) from non-frontal to frontal view where further recognition of the expressions can be performed using a state-of-the-art facial expression recognition method. To learn the mapping functions we investigate four regression models: Linear Regression (LR), Support Vector Regression (SVR), Relevance Vector Regression (RVR) and Gaussian Process Regression (GPR). Our extensive experiments on the CMU Multi-PIE facial expression database show that the proposed scheme outperforms view-specific classifiers by utilizing considerably less training data." @default.
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- W2126234799 date "2010-08-01" @default.
- W2126234799 modified "2023-10-14" @default.
- W2126234799 title "Regression-Based Multi-view Facial Expression Recognition" @default.
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- W2126234799 doi "https://doi.org/10.1109/icpr.2010.1001" @default.
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