Evaluation of Eye-Blinking Dynamics in Human Emotion Recognition using Weighted Visibility Graph - Atefeh Goshvarpour; Ateke Goshvarpour (Frontiers in Biomedical Technologies, 2024)
Currently, designing an automated emotion recognition system using biosignals becomes a hot and challenging issue in many fields, including human-computer interferences, robotics, and affective computing. Several algorithms have been proposed to characterize the internal and external behaviors of the subjects in confronting emotional events/stimuli. Eye movements, as an external behavior, are habitually analyzed in a multi-modality system using classic statistical measures, and the evaluation of its dynamics has been neglected so far. This experiment intended to provide an innovative single-modality scheme for emotion classification using eye-blinking data. For the first time, the dynamics of eye-blinking data have been characterized by weighted visibility graph-based indices. The extracted measures were then fed to the different classifiers, including support vector machine, decision tree, k-Nearest neighbor, AdaBoost, and random subset, to complete the process of classification. The proposed framework provided significant performance in terms of recognition rates. The highest average recognition rates of > 90% were achieved using the decision tree. In brief, our results showed that eye-blinking data has the potential for emotion recognition. The present system can be extended for designing future affect recognition systems.