Vol. 4 No. 2 (2024): Journal of Millimeterwave Communication, Optimization and Modelling
Articles

Facial Expression Recognition and Emotion Detection with CNN methods And SVM Classifiers

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  • NIBRAS FAROOQ AKRAM ALKHALEELİ,
  • Yaşar Becerikli
NIBRAS FAROOQ AKRAM ALKHALEELİ
Kocaeli Üniversity
Actual journals and last information about wireless communication

Published 31.07.2024

Keywords

  • SVM,
  • CNN,
  • facial expressions,
  • deeplearning,
  • machine learning,
  • classification,
  • convolutional neural network,
  • VGG16,
  • MobileNet
    • ...More
    Less

Copyright (c) 2024 NIBRAS FAROOQ AKRAM ALKHALEELİ; Yaşar Becerikli (Co-Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

There are different humans in our life. With the different languages and cultures of the human, the involuntary methods of facial and body expression remain the most realistic and honest ways. In this study, we will interpret people's emotions through facial expression. A system for detecting human emotions through facial expressions is proposed, in which we first extract facial features using deep learning methods, (VGG16 and MobileNet v1 of CNN models) and then train an SVM algorithm for emotion classification. The results showed that the properties extracted and classified by SVM are superior to the SoftMax classification method in the algorithms (VGG16, MobileNet v1) are used. We see an increase in accuracy of VGG16+SVM equal 3.07 compared to using the Softmax in VGG16. And the resulting accuracy increases by MobileNet+SVM equal 2.737 compared to MobileNet+Softmax. The second part we propose to model a hybrid neural network from each VGG with MobileNet to extract the features and then classification by SVM algorithm.

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