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- W4383670036 abstract "This paper introduces a method for human emotion recognition using facial characteristic points (FCPs) and discrete cosine transform (DCT) coefficients as the feature vectors, extracted from the human emotion facial images. In order to classify the emotions based on the feature vectors, Support Vector Machine is used as a classifier. For experimentation purpose linear function, polynomial, non-homogenous polynomial, sigmoid, Gaussian, and radial basis functions are used as kernels. In this work two types of features as facial shape features and appearance features are used for facial emotion recognition. In the first type thirty FCPs are extracted as the facial shape features, whereas in the second type first hundred coefficients of DCT are used as the feature vectors. In order to extract the FCPs, the facial image is localized and divided into various regions. Performance of the proposed algorithm has been evaluated with the help of confusion matrices, average accuracy, and timing analysis on seven human emotion facial images from Japanese Female Facial Expression (JAFFE), and in-house generated databases independently. The databases is divided in two parts as train and test databases in the ratio of 70% and 30%, respectively. The performance is compared with the amount of confusion for the same number of the class created on the same database. The highest average accuracy obtained with the proposed algorithm for emotion recognition is 92.86%. The results show that the radial basis function of SVM is more suitable for person-associated situation and the linear function describes person-irrelevant problems better." @default.
- W4383670036 created "2023-07-09" @default.
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- W4383670036 date "2023-01-01" @default.
- W4383670036 modified "2023-09-25" @default.
- W4383670036 title "Human Emotion Recognition Using Facial Characteristic Points and Discrete Cosine Transform with Support Vector Machine" @default.
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- W4383670036 doi "https://doi.org/10.1007/978-981-99-0483-9_27" @default.
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