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Emotion Detection from Facial Expression using Support Vector Machine

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Vanita Jain, Pratiksha Aggarwal, Tarun Kumar, Vaibhav Taneja
10.5120/ijca2017914398

Vanita Jain, Pratiksha Aggarwal, Tarun Kumar and Vaibhav Taneja. Emotion Detection from Facial Expression using Support Vector Machine. International Journal of Computer Applications 167(8):25-28, June 2017. BibTeX

@article{10.5120/ijca2017914398,
	author = {Vanita Jain and Pratiksha Aggarwal and Tarun Kumar and Vaibhav Taneja},
	title = {Emotion Detection from Facial Expression using Support Vector Machine},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {8},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {25-28},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume167/number8/27794-2017914398},
	doi = {10.5120/ijca2017914398},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The objective of this paper is to apply Support Vector Machine to the problem of classifying emotion on images of human faces. This well defined problem is complicated by the natural variation in people’s faces, requiring the classification algorithm to distinguish the small number of relevant features from the large pool of input features. Three different kernels i.e., linear kernel, polynomial kernel and RBF kernel are used to recognise eight facial expressions, anger, contempt, disgust, fear, happiness, neutral, sadness and surprise of human beings in still images. Accuracy of the three kernels is compared to judge the best kernel for facial expression recognition.

References

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Keywords

Facial Expression, Support Vector Machine, Emotion Detection