针对现有心理压力检测方法主观性强、准确率低,且无法连续监测的问题,提出了一种融合心率变异性(HRV)与人脸表情的非接触式心理压力检测方法。该方法通过成像式光电容积描记(IPPG)技术从视频图像中提取HRV信息,并通过VGG19网络建立表情识别模型,获得人脸表情。将HRV及表情共同作为特征输入,利用支持向量机进行训练分类,实现压力状态与非压力状态的检测。实验结果表明,本方法的压力分类准确率可达到81.4%,能有效提高心理压力检测的准确性,可应用于普通人群、运动员、犯罪人员心理测试等领域。

In order to solve the problems of subjective, low accuracy and cannot be used for continuous monitoring of existing psychological stress detection methods, a non-contact psychological stress detection method combining heart rate variability (HRV) and facial expression is proposed in this work. The method extracts HRV information from video images by image photoplethysmography technology, and obtains facial expressions by establishing expression recognition model through VGG19 network. HRV and facial expressions are used as feature inputs, and support vector machine is used for training classification to realize the detection of stress state and non-stress state. Experimental results show that the stress classification accuracy of the method can reach 81.4%, which can effectively improve the accuracy of mental stress detection. The method can be applied to psychological testing of ordinary people, athletes, criminals, and other fields.