# Recognizing Teachers' Hand Gestures for Effective Non-Verbal Interaction
## 5 Conclusions  
In this study, we proposed a hand gesture recognition framework for deep convolutional neural network models under complex backgrounds and different backlight conditions. The proposed framework incorporates deep convolutional neural networks as classifiers into hand gesture recognition tasks, which can be used for non-verbal interaction between teachers and students in the classroom. We established a two-phase machine learning task in order to improve the recognition accuracy and robustness, i.e., we detected the shape of the hand gesture at the first stage and determined the specific meanings at a later stage. A series of experiments showed that the proposed framework can outperform many existing algorithms, including YOLO, YOLOv3, and the Bayesian model, etc. The encouraging results obtained from the experiments can be attributed to multi-scale feature fusion tactics in DCNN. As the complexity of fusion features increases, the \"dropout\" method can not only avoid overfitting in DCNN, but can also effectively improve the recognition rate and greatly shorten the recognition time compared to probabilistic neural networks and support vector machines. Moreover, with the increase in the number of deep convolutional neural network layers, the \"dropout\" method can improve the robustness of gestures in complex scenes by randomly ignoring a certain proportion of node responses in each network layer. It is worth noting that although the proposed framework includes the preprocessing step and improved CNN architecture, the main performance improvement should be attributed to the improved CNN architecture, because all experiments conducted on algorithmic architectures used the preprocessed data instead of the raw data. In summary, the proposed framework is expected to be applied to real classroom scenarios for the recognition of teachers' hand gestures, because of the sound features with regard to running speed, accuracy, and robustness. However, one main limitation of this study is that we did not run the proposed framework directly on the mature dataset of teachers' hand gestures collected in real classroom settings. Rather, we adopted public datasets that satisfy the established conditions as a simulation of real classroom teaching. We believe the results are also credible and meaningful in the absence of available datasets with respect to teachers' hand gestures. Future work to be undertaken is to incorporate the proposed framework into cloud platforms of universities and validate the real performance in the classroom. Distributed implementation of the proposed framework will be extended to meet the requirement of large-scale classroom videos.  
Conceptualization, Z.P.; methodology, Z.P. and Z.Y.; software, J.X.; validation, Z.P., Z.Y. and J.X.; formal analysis, Z.Y.; investigation, T.X.; resources, Z.Y.; data curation, J.X.; writing--original draft preparation, Z.P.; writing--review and editing, T.X.; visualization, Z.P.; supervision, Z.P.; project administration, T.X.; funding acquisition, Z.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Quanzhou Fengze District Science and Technology Plan Project (2021 FZ23), Fujian Social and Science Project (FJ2022B067), Fujian Undergraduate University Teaching Reform Project (FBJG20200087, FBJG20210264), and Quanzhou Normal University New Liberal Arts Reform Project (2021 XWK006).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflict of interest.