# Recognizing Teachers' Hand Gestures for Effective Non-Verbal Interaction
## 2 Related Works
### Recognition of Hand Gestures  
Hand gesture recognition can be divided into a static approach and dynamic approach. The static approach concentrates on the inner information of an image representing the shape or size of an object. This approach typically uses a general classifier or a template matcher, which may include linear learners and non-linear learners depending on whether the data are separated linearly [28]. The recognition of static hand gestures is mainly based on relatively differentiable gestures, which can be quite similar when viewed in two dimensions [29]. However, the dynamic approach is more complicated than the static approach, and aims to explore the characteristics of spatial-temporal movements, requiring techniques that handle motion-based gesture representation [30]. Both approaches, with regard to hand gesture recognition, provide facilitative communication and natural interaction means in a diversity of real scenarios. However, they can be significantly different in terms of recognizing static and dynamic hand gestures. The choice mainly relies on the specific hand gesture representation. For example, authors in the literature [31] suggested the use of a contour line Fourier descriptor of a segmentation image obtained by mean shift algorithm to classify static hand gesture. Other authors in the literature [32] proposed classifying the given hand gesture represented by hand contour into some predefined finite number of gesture classes, such as Open, Close, Cut, Paste, Maximize, and Minimize. However, dynamic hand gestures can be modeled as state transitions with temporal or probabilistic constraints. Therefore, authors in the literature [33] proposed a hidden Gauss-Markov model with a short-term Fourier transform to identify the best likelihood gesture model for a given pattern. To recognize a hand gesture from images more efficiently, many scholars have concentrated on the power of traditional machine learning algorithms. Recursive induction learning was proposed to use extended variable-valued logic to induce rules from sets of feature vectors [34]. The locally linear embedding (LLE) algorithm was proposed in the literature [35] to map high-dimensional data to low-dimensional space, and which tried to discover the intrinsic structure and neighborhood relationship of the data based on similarity measurements. A probabilistic neural network (PNN) was used to classify different gestures based on the adaptive region-based active contour [36]. PNN has been identified with sound features of training speed, classification accuracy, and negligible retraining time. Authors in the literature [37] proposed a multi-scale and multi-angle hand gesture cognition algorithm using the single Gaussian model and SVM classifier. They processed the shape and contour of the hand gesture with the distribution of intensity gradients and edge directions so that the algorithm could capture the texture information and improve the rotation invariance of the image. Authors in the literature [38] integrated Bayesian attention, used the visual cortex model to perform feature extraction on the detected gesture regions, and then utilized the SVM classifier for hand gesture recognition. However, these methods are too complicated to calculate, and have a large time overhead, resulting in poor practicability of the algorithm. Most of them fuse hand geometric features, palm prints, and hand veins to represent the hand gestures, but the method that designs features artificially is difficult to adapt to the diversity of gestures, resulting in a low gesture recognition accuracy. Another significant limitation of these methods is that the algorithms are less robust in complex contexts [39], which means the algorithms may work appropriately in one scenario but may not obtain good performance in other scenarios.