Emotion Detection of Thai Elderly Facial Expressions using Hybrid Object Detection
Conference proceedings article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Thanapong Khajontantichaikun, Saichon Jaiyen, Siam Yamsaengsung, Pornchai Mongkolnam, and Unhawa Ninrutsirikun
Publication year: 2022
Title of series: -
Number in series: -
Volume number: -
Start page: 219
End page: 223
Number of pages: 5
URL: https://kuse.csc.ku.ac.th/icsec2022
Languages: English-United States (EN-US)
Abstract
An elderly population is a special group that needs to be taken care of closely. A key area of concern for the elderly is that of mental health and many technologies can be applied this his area. One possible tool is facial expression recognition (FER) that can be used to detect emotions of the elderly for the purpose of mental health care. In this research, we propose a hybrid of Faster R-CNN, SSD, and YOLOv5 object detection models for elderly facial expression detection. In our experiments, the proposed hybrid model is trained on a Thai elderly facial emotion dataset, and its performance is compared to a single-model of Faster R-CNN, SSD, and YOLOv5. The experimental results indicates that the proposed hybrid object detection model achieves the best performance with an accuracy of 94.07%, This is comparatively better than YOLOv5, which gives the accuracy of 93.33%.
Keywords
Artificial Intelligence, Facial Emotion Detection, Thai Elderly, YOLOv5