Affective Assessment Based on Dynamic Digital Analysis of Pupil Diameter

In recent years, emotion recognition research based on physiological signal measurements has gradually gained traction. In particular, Pupil Diameter (PD) is considered a promising physiological indicator that can intuitively reflect changes in an individual's emotional state. However, challenges persist in the denoising process of pupil signals and accuracy of emotion recognition. To address these issues, this study proposes a dual-filter denoising method and a digital classification method based on machine learning. The study aims to effectively denoise the PD signal while retaining subtle features related to emotions and improve the accuracy of assessing subjects' different emotional states. First, an emotion induction experiment is designed based on auditory and visual stimuli to guide subjects through emotional states ranging from calm to startled, stressed, and pleasant. Simultaneously, eye-tracking devices are used to collect continuous data on the PD signal. To mitigate noise in the data, cubic spline interpolation is employed to compensate for the signal loss caused by blinking and system noise from equipment. Subsequently, a dual preprocessing step using Kalman filtering and wavelet denoising is applied to the raw data. Then, using four key features extracted from the pupil data, the emotional states of the subjects are classified and compared across five classification algorithms, achieving an average accuracy of 84.38%. The performance of each model is evaluated. The Multilayer Perceptron (MLP) demonstrates the best performance, achieving the highest accuracy of 87.07%. Finally, the performance of the four features in distinguishing different emotional states is compared using Receiver Operating Characteristic (ROC) curves.