摘要： 面向在线教育中人脸视频的生理参数测量是当前智慧教育研究的热点。传统的远程光电容积描记法（rPPG）无法适应在线教育场景中的光照环境变化，影响了基于人脸视频进行生理参数测量的灵活性和准确性。面向在线教育中的典型光照场景，提出了一种基于光照自适应的血容量脉冲信号（BVP）提取方法，并结合生成对抗网络（GAN）与卷积神经网络（CNN）构建了BVP信号双重校正模型。首先基于模拟退火算法计算不同光照条件下正交色度信号的最优解。同时，建立利用平均灰度强度进行光照场景分类的光照场景预测机制，实现光照场景自适应的最优色度信号。进一步结合GAN与CNN模型对BVP信号进行双重校正，以确保最终输出的生理参数更加准确可靠。模型在面向典型教育场景重组的四个公开数据集上进行了验证，实验结果表明，心率的均方根误差（RMSE）平均降低了8.3 bpm，展示了该模型在不同光照条件下的鲁棒性和准确性。该模型在提升心率及心率变异性预测准确性方面具有显著优势，可为复杂光照环境下的非接触式生理参数检测提供有效支持。

Abstract: The measurement of physiological parameters from facial videos in online education is currently a research hotspot in intelligent education. Traditional remote photoplethysmography (rPPG) cannot adapt to the changes in the lighting environment in online education scenarios, which affects the flexibility and accuracy of physiological parameter measurement based on facial videos. Aiming at the typical lighting scenarios in online education, a method for extracting blood volume pulse (BVP) signals based on lighting adaptability is proposed, and a dual correction model for BVP signals is constructed by combining a generative adversarial network (GAN) and a convolutional neural network (CNN). Firstly, the optimal solution of the orthogonal chrominance signal under different lighting conditions is calculated based on the simulated annealing algorithm. At the same time, a lighting scene prediction mechanism for classifying lighting scenes using the average gray intensity is established to achieve the optimal chrominance signal that adapts to the lighting scene. Furthermore, the GAN and CNN models are combined to perform dual correction on the BVP signal to ensure that the finally output physiological parameters are more accurate and reliable. The model is verified on four publicly available datasets reorganized for typical educational scenarios. The experimental results show that the root mean square error (RMSE) of the heart rate is reduced by an average of 8.3 bpm, demonstrating the robustness and accuracy of the model under different lighting conditions. This model has significant advantages in improving the accuracy of heart rate and heart rate variability prediction, and can provide effective support for contactless physiological parameter detection in complex lighting environments.