Facial Emotion Recognition System for Isolated Patient Monitoring using ResNet-50 based Convolutional Neural Network Model

Abstract

Facial emotion recognition is a promising technology that has the potential to improve the quality of care for isolated patients by enabling healthcare professionals to assess the emotional state of these patients more accurately. Isolated patient monitoring is the practice of closely observing patients who have been isolated due to the risk of infection or other health concerns, and it is essential to ensure the well-being of these patients. The Facial Expression Recognition system is the process of identifying the emotional state of a person. In this system, captured image is compared with the trained dataset available in database and then emotional state of the image will be displayed. This will also integrate the system into existing patient monitoring systems and collect data on its effectiveness in improving the care and well-being of isolated patients. The ultimate goal is to demonstrate the value of facial emotion recognition in isolated patient monitoring, and to identify any potential ethical or privacy concerns that may need to be addressed. This is for facial emotion recognition (FER) in isolated patient monitoring. FER is achieved using computer vision techniques and voice recognition techniques to analyze and interpret facial expressions, which can be useful for identifying and tracking changes in patient’s emotional states. The proposed FER system utilizes deep learning algorithms for real-time detection and classification of emotions with high accuracy. Two popular methods utilized mostly in the literature for the automatic FER systems are based on geometry and appearance. Facial Expression Recognition usually performed in three-stages consisting of pre-processing, face detection & feature extraction, and expression classification. The paper analyses various deep learning image classification models (convolutional neural networks) to identify the certain human emotions: depressed, despair, fear, irritated, uneasy, shocked and neutrality.