Emotion Detection of Thai Elderly Facial Expressions using Hybrid Object Detection

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%.