On Developing Facial Stress Analysis and Expression Recognition Platform

TL;DR

This paper develops and tests facial expression recognition and stress analysis algorithms using neural networks for an immersive learning platform, improving accuracy and response speed in real-time applications.

Contribution

It introduces new real-time facial analysis algorithms that overcome hardware and training limitations, enhancing accuracy and speed for educational systems.

Findings

Improved facial expression recognition accuracy.

Enhanced real-time response speed.

Better heart rate detection compared to social equipment.

Abstract

This work represents the experimental and development process of system facial expression recognition and facial stress analysis algorithms for an immersive digital learning platform. The system retrieves from users web camera and evaluates it using artificial neural network (ANN) algorithms. The ANN output signals can be used to score and improve the learning process. Adapting an ANN to a new system can require a significant implementation effort or the need to repeat the ANN training. There are also limitations related to the minimum hardware required to run an ANN. To overpass these constraints, some possible implementations of facial expression recognition and facial stress analysis algorithms in real-time systems are presented. The implementation of the new solution has made it possible to improve the accuracy in the recognition of facial expressions and also to increase their response speed. Experimental results showed that using the developed algorithms allow to detect the heart rate with better rate in comparison with social equipment.