摘要
计算机视觉在智能感知领域发挥着重要作用。现有的心理状态感知方法仅局限于面部表情识别或远程光电容积描记术等单一任务,难以实现多维特征的协同感知;而融合多模态生理信号的方法则面临较高的计算成本。针对这些问题,本文提出一种基于多任务轮换学习的非接触式心理状态感知方法。该方法通过多任务模型处理人脸视频,同时完成rPPG心率信号提取、情感指标预测和心理状态分类3个任务。实验结果表明,该模型在rPPG心率信号提取上的平均绝对误差为3.78,情感效价和唤醒度预测的准确率分别为97.47%和96.75%,心理状态分类的准确率为97.42%。该方法为非接触式心理状态感知提供了一种高效的多任务处理方案,具有重要的理论和实践价值。
Computer vision plays a crucial role in the field of intelligent perception.Existing methods for psychological state perception are typically limited to single tasks such as facial expression recognition or remote photoplethysmography,making it difficult to achieve collaborative perception of multidimensional features.Additionally,approaches that integrate multimodal physiological signals face high computational costs.To address these challenges,this paper proposes a non-contact psychological state perception method based on multi-task rotation learning.The proposed approach utilizes a multi-task model to process facial video,simultaneously performing three tasks:rPPG heart rate signal extraction,emotional valence and arousal prediction,and psychological state classification.Experimental results show that the model achieves an average absolute error of 3.78 for rPPG heart rate signal extraction,prediction accuracies of 97.47%and 96.75%for emotional valence and arousal,respectively,and a classification accuracy of 97.42%for psychological state.This method provides an efficient multi-task processing solution for non-contact psychological state perception,offering significant theoretical and practical value.
作者
王宇
牛知艺
Wang Yu;Niu Zhiyi(College of Aviation Electronic and Electrical Engineering,Civil Aviation Flight University of China,Chengdu 641450,China)
出处
《电子测量技术》
2025年第24期195-203,共9页
Electronic Measurement Technology
基金
国家自然科学基金青年基金(62406207)
四川省自然科学基金青年基金(2025ZNSFSC1502)
中央高校基本科研业务费(25CAFUC03023)资助
