融合心率变异性与表情的非接触心理压力检测

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.