Fuzzy Model on Human Emotions Recognition

TL;DR

This paper presents a fuzzy model for recognizing multiple human emotions through keyboard, mouse, and touchscreen interactions, aiming to improve emotion detection accuracy in human-computer interaction.

Contribution

It introduces a fuzzy-based approach for multi-level emotion recognition that can detect a broader range of emotions compared to traditional methods.

Findings

The fuzzy model detects more emotions than non-fuzzy methods.

Accuracy varies across different emotions, with some lower than traditional methods.

The system was trained and tested using SVM for emotion recognition.

Abstract

This paper discusses a fuzzy model for multi-level human emotions recognition by computer systems through keyboard keystrokes, mouse and touchscreen interactions. This model can also be used to detect the other possible emotions at the time of recognition. Accuracy measurements of human emotions by the fuzzy model are discussed through two methods; the first is accuracy analysis and the second is false positive rate analysis. This fuzzy model detects more emotions, but on the other hand, for some of emotions, a lower accuracy was obtained with the comparison with the non-fuzzy human emotions detection methods. This system was trained and tested by Support Vector Machine (SVM) to recognize the users' emotions. Overall, this model represents a closer similarity between human brain detection of emotions and computer systems.