# Recognizing Teachers' Hand Gestures for Effective Non-Verbal Interaction
## 1 Introduction
Although the accuracy of target classification was improved to a certain extent, the algorithm was less robust to noise and had insufficient performance for low-brightness target detection. An Adaboost face detection algorithm based on statistical transformation was proposed in the literature [13]. The algorithm built a weak classifier for each pixel for ensemble training, which can effectively segment similar areas of the image. Since all pixels needed to be trained, the training time of the algorithm increased sharply with the increase in the input image resolution. Furthermore, when the background of the input image is abruptly changed, the accuracy of the algorithm decreases significantly. More recently, scholars proposed you only look once (YOLO), YOLOv2, YOLOv3 deep learning frameworks [14]. These frameworks improved the speed of detection while taking into account the accuracy of small target recognition. However, they often extract deep features by designing deeper network structures, which require high computing power of hardware and cannot be applied to real classroom scenarios. Successful estimation of teachers' hand gestures is a difficult task because the complexity of teachers' surrounding environment and backlight differences can significantly affect hand gesture recognition accuracy. This imposes severe restrictions on teachers' hand gesture recognition in the classroom environment. First, hand gesture pictures taken in complex environments contain a lot of noise, which will be extracted into the feature space and interfere with the recognition of normal images. For example, a gesture with a clean background is shown in Figure 1a, and gestures with complex backgrounds are shown in Figure 1b-d. Clearly, recognizing hand gestures against a complex background is much more difficult than recognizing hand gestures against a clean background. In addition, the recognition accuracy of teachers' hand gestures is also affected by the light intensity. The current algorithms are mainly divided into color-based detection algorithms, shape-based detection algorithms, and deep-learning-based detection algorithms [15]. Color-based detection is mainly based on RGB and HSV (hue, saturation, value) color spaces, creating a saliency map by segmenting specific colors, which can detect candidate regions quickly and efficiently. However, color-based detection is easily affected by light, and cannot meet the requirements of hand gesture recognition under different backlight conditions [16]. Shape-based detection utilizes the particularity of human gestures in shape to detect their edges. However, when the teacher's hand gesture is blurred or occluded by motion, the accuracy of the algorithm will decrease greatly. In order to effectively solve the problem of hand gesture recognition under complex backgrounds and different backlight conditions, this paper proposes an improved Adaboost hand gesture recognition algorithm based on key point statistical transformation features, called the modified census transform (MCT). First, we extract the MCT features of the image to increase the robustness of the algorithm to illumination changes, enabling the estimation of teachers' hand gestures under poor lighting. Second, we extract the key points of the image to construct the Adaboost ensemble classifier, which can effectively shorten the training time and meet the needs of real-time detection on the premise of improving the algorithm's anti-occlusion ability. Finally, the extracted target features are fused through a multi-scale neural network, so as to improve the accuracy of teachers' hand gesture recognition under different backlights.