On Developing Facial Stress Analysis and Expression Recognition Platform
This work addresses the need for efficient real-time facial analysis in digital learning platforms, though it appears incremental as it builds on existing ANN methods.
The authors tackled the challenge of implementing facial expression recognition and stress analysis in real-time systems by developing new algorithms that improve accuracy and response speed, with experimental results showing better heart rate detection compared to existing equipment.
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.