Perfecting a Video Game with Game Metrics
Vol 18, No 5: October 2020

Development of video-based emotion recognition using deep learning with Google Colab

Teddy Surya Gunawan (International Islamic University Malaysia)
Arselan Ashraf (International Islamic University Malaysia)
Bob Subhan Riza (Universitas Potensi Utama)
Edy Victor Haryanto (Universitas Potensi Utama)
Rika Rosnelly (Universitas Potensi Utama)
Mira Kartiwi (International Islamic University Malaysia)
Zuriati Janin (Universiti Teknologi MARA)



Article Info

Publish Date
01 Oct 2020

Abstract

Emotion recognition using images, videos, or speech as input is considered as a hot topic in the field of research over some years. With the introduction of deep learning techniques, e.g., convolutional neural networks (CNN), applied in emotion recognition, has produced promising results. Human facial expressions are considered as critical components in understanding one's emotions. This paper sheds light on recognizing the emotions using deep learning techniques from the videos. The methodology of the recognition process, along with its description, is provided in this paper. Some of the video-based datasets used in many scholarly works are also examined. Results obtained from different emotion recognition models are presented along with their performance parameters. An experiment was carried out on the fer2013 dataset in Google Colab for depression detection, which came out to be 97% accurate on the training set and 57.4% accurate on the testing set.

Copyrights © 2020






Journal Info

Abbrev

TELKOMNIKA

Publisher

Subject

Computer Science & IT

Description

Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of ...