Performance enhancement of facial electromyogram-based facial-expression recognition for social virtual reality applications using linear discriminant analysis adaptation

Recent studies have indicated that facial electromyogram (fEMG)-based facial-expression recognition (FER) systems are promising alternatives to the conventional camera-based FER systems for virtual reality (VR) environments because they are economical, do not depend on the ambient lighting, and can be readily incorporated into existing VR headsets. In our previous study, we applied a Riemannian manifold-based feature extraction approach to fEMG signals recorded around the eyes and demonstrated that 11 facial expressions could be classified with a high accuracy of 85.01%, with only a single training session. However, the performance of the conventional fEMG-based FER system was not high enough to be applied in practical scenarios. In this study, we developed a new method for improving the FER performance by employing linear discriminant analysis (LDA) adaptation with labeled datasets of other users. Our results indicated that the mean classification accuracy could be increased to 89.40% by using the LDA adaptation method ( p