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All Journal Knowledge Engineering and Data Science
Alwin Melkie Sambul
Sam Ratulangi University
Computer Science & IT Engineering

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Human Facial Expressions Identification using Convolutional Neural Network with VGG16 Architecture Luther Alexander Latumakulita; Sandy Laurentius Lumintang; Deiby Tineke Salakia; Steven R. Sentinuwo; Alwin Melkie Sambul; Noorul Islam
Knowledge Engineering and Data Science Vol 5, No 1 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i12022p78-86

Abstract

The human facial expression identification system is essential in developing human interaction and technology. The development of Artificial Intelligence for monitoring human emotions can be helpful in the workplace. Commonly, there are six basic human expressions, namely anger, disgust, fear, happiness, sadness, and surprise, that the system can identify. This study aims to create a facial expression identification system based on basic human expressions using the Convolutional Neural Network (CNN) with a 16-layer VGG architecture. Two thousand one hundred thirty-seven facial expression images were selected from the FER2013, JAFFE, and MUG datasets. By implementing image augmentation and setting up the network parameters to Epoch of 100, the learning rate of 0,0001, and applying in the 5Fold Cross Validation, this system shows performance with an average accuracy of 84%. Results show that the model is suitable for identifying the basic facial expressions of humans.
Co-Authors Deiby Tineke Salaki Luther Latumakulita Noorul Islam Sandy Laurentius Lumintang Steven Ray Sentinuwo