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• W2802319772 abstract "In this work, a new approach for facial expression recognition has been proposed. The approach has imbedded in it both new feature extraction technique and classification techniques using automatic auto-tuning of kernel parameter optimization in support vector machines. It generally begins with feature extraction from the input vectors using a combination of arithmetic means difference and rotation invariant Local Binary Pattern. The extracted features are projected into a Gaussian space to match it with the radial basis function kernel used in support vector machines for classification. Prior to classification, an optimized parameter for support vector machines training are automatically determined based on an approach proposed which relies on the receiver operating characteristics of the support vector machine classifier. The results obtained from the experiments were impressive and promising. From the experiments conducted on the two facial expression databases with different cross-validation techniques, the proposed approach outperforms its counterparts under the same database and settings." @default.
• W2802319772 created "2018-05-17" @default.
• W2802319772 creator A5038519736 @default.
• W2802319772 creator A5048376854 @default.
• W2802319772 date "2018-03-01" @default.
• W2802319772 modified "2023-09-26" @default.
• W2802319772 title "Facial expression recognition with an optimized radial basis kernel" @default.
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• W2802319772 doi "https://doi.org/10.1109/isdfs.2018.8355366" @default.
• W2802319772 hasPublicationYear "2018" @default.
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