A Facial Expression Recognition Model using Support Vector Machines

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Sivaiah Bellamkonda 1,* N.P.Gopalan 1

1. Department of Computer Applications, National Institute of Technology, Tiruchirappalli - 620015, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijmsc.2018.04.05

Received: 11 Nov. 2017 / Revised: 16 May 2018 / Accepted: 19 Jul. 2018 / Published: 8 Nov. 2018

Index Terms

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


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.

Cite This Paper

Sivaiah Bellamkonda, N.P. Gopalan,"A Facial Expression Recognition Model using Support Vector Machines", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.4, No.4, pp.56-65, 2018. DOI: 10.5815/ijmsc.2018.04.05


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