Recognition of Human Emotion using Radial Basis Function Neural Networks with Inverse Fisher Transformed Physiological Signals

Authors

  • Abdultaofeek Abayomi Durban University of Technology
  • Oludayo O Olugbara Durban University of Technology, Durban, South Africa.
  • Delene Heukelman Durban University of Technology, Durban, South Africa.

Keywords:

Neural network, human emotion, physiological signal, feature extraction, fisher transform

Abstract

Emotion is a complex state of human mind influenced by body physiological changes and interdependent external events thus making an automatic recognition of emotional state a challenging task. A number of recognition methods have been applied in recent years to recognize human emotion. The motivation for this study is therefore to discover a combination of emotion features and recognition method that will produce the best result in building an efficient emotion recognizer in an affective system. We introduced a shifted tanh normalization scheme to realize the inverse Fisher transformation applied to the DEAP physiological dataset and consequently performed series of experiments using the Radial Basis Function Artificial Neural Networks (RBFANN). In our experiments, we have compared the performances of digital image based feature extraction techniques such as the Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and the Histogram of Images (HIM). These feature extraction techniques were utilized to extract discriminatory features from the multimodal DEAP dataset of physiological signals. Experimental results obtained indicate that the best recognition accuracy was achieved with the EEG modality data using the HIM features extraction technique and classification done along the dominance emotion dimension. The result is very remarkable when compared with existing results in the literature including deep learning studies that have utilized the DEAP corpus and also applicable to diverse fields of engineering studies.

Downloads

Download data is not yet available.

Downloads

Published

31-08-2021

How to Cite

Abayomi, A. ., Olugbara, O. O., & Heukelman, D. (2021). Recognition of Human Emotion using Radial Basis Function Neural Networks with Inverse Fisher Transformed Physiological Signals. International Journal of Integrated Engineering, 13(6), 1–26. Retrieved from https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/5243

Issue

Section

Articles