A facial expression recognition model using support vector machines

Автор: Sivaiah Bellamkonda, N.P. Gopalan

Журнал: International Journal of Mathematical Sciences and Computing @ijmsc

Статья в выпуске: 4 vol.4, 2018 года.

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Facial Expression Recognition (FER) has gained interest among researchers due to its inevitable role in the human computer interaction. In this paper, an FER model is proposed using principal component analysis (PCA) as the dimensionality reduction technique, Gabor wavelets and Local binary pattern (LBP) as the feature extraction techniques and support vector machine (SVM) as the classification technique. The experimentation was done on Cohn-Kanade, JAFFE, MMI Facial Expression datasets and real time facial expressions using a webcam. The proposed methods outperform the existing methods surveyed.

Facial Expression Recognition, Principal Component Analysis, Support vector Machine, Gabor Wavelets, Local Binary Pattern, Machine Vision, Human Computer Interaction

Короткий адрес: https://sciup.org/15016680

IDR: 15016680 | DOI: 10.5815/ijmsc.2018.04.05

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