Abstract:

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.

Key words: affective assessment, Pupil Diameter (PD), Kalman filtering, wavelet denoising, Walsh transform, machine learning, Receiver Operating Characteristic (ROC) curves

摘要：

近年来, 基于生理信号测量的情感识别研究逐渐兴起, 其中, 瞳孔直径(PD)被认为是一种有潜力的生理指标, 可以直观反映出个体的情感状态变化。然而, 瞳孔信号的降噪处理以及情感识别精度仍然面临挑战。为了解决上述问题, 提出一种双重滤波的降噪方法以及一种基于机器学习的数字化分类方法, 旨在对PD信号进行有效去噪的同时保留与情感相关的细微特征, 以及提高对受试者不同情感状态评估的准确率。首先, 设计基于听觉与视觉刺激的情感诱导实验, 引导受试者的情感状态从平静到惊吓、压力以及愉悦, 同时使用了眼动仪采集其PD信号在连续时间段内的数据。为应对数据中的噪声, 采用三次样条插值法弥补由眨眼及设备系统噪声引起的信号缺失, 再采用卡尔曼滤波与小波去噪对原始数据进行双重预处理。然后, 利用从瞳孔数据中提取的4个关键特征, 用5种分类算法对受试者的情感状态进行分类并比对了各个模型的性能指标, 达到84.38%的平均准确率。其中, 多层感知器(MLP)的效果最佳, 达到了87.07%的最高准确率。最后, 通过接收者操作特征(ROC)曲线对比了4种特征在区分不同情感状态方面的性能。

关键词: 情感评估, 瞳孔直径, 卡尔曼滤波, 小波去噪, 沃尔什变换, 机器学习, 接收者操作特征曲线