# Recognizing Teachers' Hand Gestures for Effective Non-Verbal Interaction
## 4 Experiments
### Experimental Results  
#### 4.4.1 Detection of Hand Gestures with Complex Backgrounds  
The running results of the proposed framework, YOLO, and YOLOv3 are shown in Figure 7. The ROC curve was obtained by continuously reducing the probability threshold of the predicted gesture area. It can be seen that the hand gesture detection effect of the YOLO algorithm is the worst and the detection effect of YOLOv3 is improved compared with the YOLO algorithm. The proposed framework achieves higher detection accuracy under different probability thresholds than both YOLO and YOLOv3. The ROC curves of the proposed framework, Bayesian attention model, DPM model, and skin-color-based HSV model are shown in Figure 8. The proposed framework can obtain higher detection accuracy compared to other models. The DPM model and the skin-color-based HSV model first obtain poor detection accuracy, but gradually surpass the Bayesian attention model. The recognition accuracy of the Bayesian attention model is more stable than other models. The detection accuracy of the DPM model seems to be worse compared to the skin-color-based HSV model. The running times of the proposed framework, Bayesian attention model, DPM model, and skin-color-based HSV model are shown in Figure 9. Figure 9 shows that the proposed framework needs no more than 35 min, the skin-color-based HSV model needs more than 90 min, and the Bayesian attention model needs about 88 min on the dataset. Clearly, the proposed framework needs the least running time to obtain the result. Figure 8: ROC curves of Bayesian attention model, DPM model, skin-color-based HSV model, and the proposed framework. Figure 7: ROC curves of YOLO, YOLOv3, and the proposed framework. The convergence of the proposed framework, Bayesian attention model, DPM model, and skin-color-based HSV model are shown in Figure 10. Figure 10 shows that the Bayesian attention model, DPM model, and skin-color-based HSV model converge at around 30 epochs. However, the proposed framework converges faster than other models. The confusion matrix of NUS-II is provided in Figure 11. The data in Table 1 show that YOLOv3 has an improved detection accuracy and localization accuracy compared to YOLO, but the proposed framework achieves the best hand gesture detection accuracy and localization accuracy among the algorithms. Table 1 also shows the detection accuracy and localization accuracy of the Bayesian model, DPM model, skin-color-based HSV model,  
Figure 10: Convergence of the Bayesian attention model, DPM model, skin-color-based HSV model, and the proposed framework. Figure 9: Running times of the Bayesian attention model, DPM model, skin-color-based HSV model, and the proposed framework. and the proposed framework. Since the skin-color-based HSV model and the Bayesian attention model are only detection algorithms that are specially applied to human hands, they do not belong in the category of general object detection algorithms, and do not have localization accuracy indicators. Since there is a large number of skin colors in the dataset, and human body and face skin color is very similar to the color of the hand, it is difficult for the skin-color-based HSV model to distinguish the gesture area, resulting in poor detection accuracy. The DPM model is based on HOG features. The skin-like and skin-colored backgrounds in the dataset reduce the gradient of pixel intensities at the gesture contour, which seriously affect the detection performance of DPM. The human body in the image causes many gradients and complex textures around the gesture, which is also the reason the DPM cannot accurately identify the gesture area. The Bayesian attention model uses the human visual cortex mechanism [57] to extract features from the shape and texture of gestures in images, and then uses the Bayesian model for hand gesture detection. The visual cortex model responsible for feature extraction only has a four-layer network structure, and the number of filters is not sufficient.