Chapter 3 - Patient Facial Emotion Recognition and Sentiment Analysis Using Secure Cloud With Hardware Acceleration

Abstract

This chapter conducts sentiment analysis of medical patients through facial emotion recognition. In this work, a patient's facial expression is dynamically captured from a video stream, recognized using machine learning-based image classifiers, and put into one of the result categories including angry, disgusted, fearful, happy, sad, surprised, and neural. The system design includes three primary components: a video processor, a face detector, and an image classifier. The video processor captures a frame with a patient face from its video feed, converting it to a grayscale image that is then passed to the face detector. The face detector performs face detection by cropping the image around the face. The cropped face image is then resized and fed into the image classifier that will eventually recognize the facial emotion.