Intelligent System Based on Wavelets for Automatic Facial Emotion Recognition

Fiorella Andrea Alejos Yarasca, Santiago Domingo Moquillaza Henriquez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Facial emotion recognition is very important for social communication. Whereby through the years it has done many studies and researches about automatic emotion recognition. Generally, the facial emotion recognition systems are composed of the pre-processing phase, the features extraction phase and the classification phase. This article proposes a method for automatic facial emotion recognition in digital images. This method uses histogram equalization to improve special lighting conditions during the pre-processing of images, two-dimensional discrete wavelet transform and PCA algorithms are used to extract and reduce features. Finally it uses a support vector machine linear to predict emotions. The experiments were made using the JAFFE database and CK+. The method achieves more than 93% for average accuracy and it recognizes better the following emotions: Happiness, neutral and surprise. For comparisons, two kinds of classifiers were adopted: Support vector machine and Convolutional Neural Network. Using this last classifier we achieve more than 98% for average accuracy.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728183671
DOIs
StatePublished - 21 Oct 2020
Event2020 IEEE Engineering International Research Conference, EIRCON 2020 - Lima, Peru
Duration: 21 Oct 202023 Oct 2020

Publication series

NameProceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020

Conference

Conference2020 IEEE Engineering International Research Conference, EIRCON 2020
Country/TerritoryPeru
CityLima
Period21/10/2023/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Facial Emotion Recognition
  • Facial Expression Classification
  • Features Extraction
  • Images Pre-processing

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