Performance Study of Facial Expression Recognition Using Convolutional Neural Network

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

6 Citations (Scopus)

Abstract

Facial expression depicts human emotions. Recognition of facial expression is used in various fields, such as for a better understanding of the customer's desires during a home design consultation and to find out the pain suffered by a patient during medical treatment. This research explores deep learning techniques based on Convolutional Neural Network (CNN) on facial expression recognition. The three pre-trained CNN models, namely VGG16, Resnet50, and Senet50, are retrained using different learning rate values and optimization functions. Trials on The Extended Cohn-Kanade Dataset (CK +) consisting of 7 expression classes, namely anger, neutral, disgust, fear, joy, sadness, and surprise, produce the best accuracy of 97% obtained by the VGG16 architecture with Adam's optimization function and learning rate of 0.001.

Original languageEnglish
Title of host publication2020 6th International Conference on Science in Information Technology
Subtitle of host publicationEmbracing Industry 4.0: Towards Innovation in Disaster Management, ICSITech 2020
EditorsAnita Ahmad Kasim, Andri Pranolo, Leonel Hernandez, Aji Prasetya Wibawa, Roman Voliansky, Hajra Rasmita Ngemba, Rafal Drezewski, Zachir Zachir, Haviluddin Haviluddin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages121-126
Number of pages6
ISBN (Electronic)9781728173498
DOIs
Publication statusPublished - 21 Oct 2020
Event6th International Conference on Science in Information Technology, ICSITech 2020 - Palu, Indonesia
Duration: 21 Oct 202022 Oct 2020

Publication series

Name2020 6th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Disaster Management, ICSITech 2020

Conference

Conference6th International Conference on Science in Information Technology, ICSITech 2020
Country/TerritoryIndonesia
CityPalu
Period21/10/2022/10/20

Keywords

  • CNN
  • Facial Expression Recognition
  • Resnet50
  • Senet50
  • VGG16

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