Sistema de detección de emociones mediante el análisis de indicadores faciales empleando inteligencia artificial

Abstract

Emotions are essential in several areas of life, however, their detection and understanding can become complicated, this will lead to misunderstandings and hinder communication, negatively impacting people's social relationships. In this context, the research was carried out with the aim of implementing an emotion detection system by analyzing facial indicators using artificial intelligence and visualizing the emotions that a person may have for a period of time. The system is divided into three stages, starting with the acquisition and processing of data through the activation and use of a webcam, supported by the OpenCV library for image processing techniques. The training phase involves the development of a deep learning model using Convolutional Neural Networks from facial recognition using FaceNet, perfected its design through data fitting, the architecture of the neural network focused on the extraction and learning of relevant features. Finally, the storage and visualization stage, the data is processed by the Jetson Nano and sent to a web hosting environment that receives the results and transmits them to the administrative interface for the management and visualization of the user's emotion report. The test results indicated that the system captured frames every 4 seconds, and boasts a classification accuracy of 92%, considering that the model has an outstanding ability to classify emotions in real time.