A Live Emotions Predictor System Using Convolutional Neural Networks

Abstract

Facial expression recognition has been an active research area, with an increasing number of applications like avatar animation, cyber security, and neuromarketing. The use of neural networks and data science are having a strong growth in research centers and universities; the field of machine learning is booming because it is a strong tool and it has an immense amount of applications. The purpose of this paper is the development of a Live Emotions Predictor using Convolutional Neural Networks, this was developed in different sections, the part of data processing and its own training using a Convolutional Neural Network (CNN) that generates accurate and precise predictions of the 5 main emotions in a graphical way. For the processing part it is important to have data that can be trained, preprocessing, and thus be able to have better results. The data generated by iMotion® are CSV files and the first part was to be able to have a clean database for its training. In the training part, the challenge was to generate a sufficiently robust CNN so we can obtain highly reliable "accuracy's" (percentages greater than 88%), determining the main architecture and all its layers to obtain these results.