Facial Emotion Detection through Deep Covolutional Neural Networks

Authors

  • Aymun Saif Dar School of Systems and Technology, University of Management and Technology, Lahore
  • Sheraz Naseer School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
  • Aihtshan Ali School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
  • Ishmal Sauf School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
  • Muhammad Ahsan School of Systems and Technology, University of Management and Technology, Lahore, Pakistan

DOI:

https://doi.org/10.21015/vtcs.v15i3.522

Abstract

Our society has evolved to a threshold where use of machines to automate mundane tasks is constantly increasing in daily life. Providing machines with capability to develop perception from their environment can lead them to perform a great variety of tasks. Facial emotion detection is crucial sub-part of machine perception development. In this article we present a deep learning based approach for Facial emotion Detection. Our model uses a Convolutional Neural Network (CNN) to learn deep features for classification of facial images into one of 22 emotion (Basic 7 + Compound 15) categories considered in this study. We trained our CNN model with the images dataset from Martinez et al. Our Facial Emotion Detection model was developed using keras with theano backend and implemented on a GPU-powered testbed. Our model achieved 67.6% accuracy for basic emotions and 33% accuracy for compound emotions. 

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Published

2018-11-04

How to Cite

Dar, A. S., Naseer, S., Ali, A., Sauf, I., & Ahsan, M. (2018). Facial Emotion Detection through Deep Covolutional Neural Networks. VAWKUM Transactions on Computer Sciences, 6(1), 70–77. https://doi.org/10.21015/vtcs.v15i3.522

Issue

Vol. 6 No. 1 (2018): January-December

Section

Articles

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