Support Vector Machines Models for Human Decision-Making Understanding: A Different Perspective on Emotion Detection

The high integration of artificial intelligence (AI) into our daily life has led to get much research on the technology’s potential, particularly with its ability to understand human emotions. Our study mainly focuses on the potential of Support Vector Machine (SVM) models in facial emotion recognition (FER) and examines the possibility to analyse the results of emotion detection through the Viable Systems Approach (VSA) perspective. The understanding of emotions as an important difference between human and machine is an ongoing issue, underlining the necessity for AI to incorporate emotional intelligence. The main objective of the project is to fill the knowledge gap existing between AI and human surroundings, ethics, and social factors. From an experimental point of view, we realized three different SVM models based on the most widely used kernel functions (linear, polynomial, and radial). Then, we used the “JAFFE” dataset to test the models on three different configurations of the initial data, to understand which parameters are most influential for the performance of the classifiers and to investigate the limitations and potential of SVMs for emotion recognition. The next step addressed in the study is to integrate computer science with VSA, providing a fresh perspective on emotion detection. This approach is not just about developing a framework for human-computer interaction (HCI) but delves deeper into understanding the social dynamics underlying decision-making. In conclusion, our paper emphasizes the significance of emotional aspects in HCI and the potential of AI in understanding human emotions. By employing the VSA, it extends the discussion on AI’s capabilities in complex decision-making processes, highlighting the necessity for AI systems to resonate cognitively with human users in increasingly digital environments.