Fig. 1. Infrared array sensor，（a）physical view of the sensor，（b）infrared temperature image（palm）

Fig. 2. System composition design

Fig. 3. Flow chart of the head motion detection algorithm

Fig. 4. Original image and pseudo-color image（the region of human head and shoulder）（a）origin image（32×32），（b）pseudo-color image（32×32），（c）pseudo-color image（64×64）

Fig. 5. Flowchart of the head salient region extraction algorithm

Fig. 6. Comparison of preprocessed results

Fig. 7. 3D image fusion of a sequence of frames

Fig. 8. Residual learning structure of ResNeXt network，（a）BottleNeck structure of ResNet network，（b）split-transform-merge structure of Inception network，（c）block structure of ResNeXt network

Fig. 9. Pro-ResNeXt50 network

Fig. 10. The training accuracy and loss of three network

Fig. 11. Experience scenarios（a）Experiments in an indoor hall，（b）Experiments in a car：Experiments were conducted in an indoor hall to simulate a driving and online learning environment，as shown in Fig. 11（a）. The test user is sitting on a chair，and the sensor is fixed at the height of 1.2 m above the ground by a tripod so that it is aligned with the user’s front face. The collection distance ranges of 0.5 m to 1 m，and the collected lighting environment includes both day and night conditions.

Fig. 12. Accuracy using different methods

Fig. 13. Random continuous head movement steering

Fig. 14. Recognition accuracy in different detection distances and light conditions

Table 0. [in Chinese]

Table 1. HTPA infrared sensor specification parameters

Table 2. Comparison of the three networks

Table 3. The Precision for each activity

Table 4. Accuracy using different channels

Table 5. Accuracy and time using different network

Table 6. The Precision for each activity in the car