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- W2031094618 abstract "Although several methods have been proposed for fusing different image representations obtained by different preprocessing methods for emotion recognition from the facial expression in a given image, the dependencies and relations among them have not been much investigated. In this study, it has been shown that covariates obtained by Canonical Correlation Analysis (CCA) that extracts relations between different representations have high predictive power for emotion recognition. As high prediction accuracy can be achieved using a small number of features extracted by it, CCA is considered to be a good dimensionality reduction method. For our simulations, we used the CK+ database and showed that covariates obtained from difference-images and geometric-features representations have high prediction accuracy." @default.
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- W2031094618 date "2012-04-01" @default.
- W2031094618 modified "2023-10-17" @default.
- W2031094618 title "Feature extraction for facial expression recognition by canonical correlation analysis" @default.
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- W2031094618 doi "https://doi.org/10.1109/siu.2012.6204837" @default.
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