Facial Recognition of Feelings in Recovering Patients

Abstract

Automated detection of facial expressions has many applications in different areas such as disease detection, video games, robotics, 3D animation, among others, because they provide extensive information based on facial expressions. Currently they have made great advances in face detection, facial recognition, gender recognition, age and gestures that allow the analysis of diseases, psychological conditions of people among other applications. Consequently, this study focuses on the detection of the feeling of sadness in recovering patients. The main objective of this proposal is to generate an algorithm for stopping the feeling of sadness through the different facial features in real time, using Artificial Vision with Python together with the OpenCV library and for linear regression statsmodels that allowed the analysis and prediction of the data. of the patient. This prototype is divided into two phases, development of the emotion detector algorithm and data analysis using linear regression. The results of this study showed a confidence level of 81% and a linear prediction of 76% based on the variables of time and feeling of sadness