# Skeleton-Based Dynamic Hand Gesture Recognition Using an Enhanced Network with One-Shot Learning
## 1 Introduction  
With the rapid development of Kinect, Leap Motion, and other sensors in recent years, hand motion capture is getting much more efficient. By estimating the posture of the hand gesture, the position information of each joint can be detected from video or image sequences. Recent research [1; 2; 3; 4; 5] has tried various ways for dynamic hand gesture recognition based on 3D skeleton data characterized as strong correlations, temporal continuity, and co-occurrence relationships. Besides, the skeleton-based algorithm has fewer parameters, which is easier to calculate and more suitable for analyzing dynamic hand gestures. However, it is still challenging because hands are non-rigid objects, which can express a variety of different semantics [6]. With the gesture recognition technology being applied in more fields such as gaming and industry training, it is often necessary to make different customized annotation samples in large sizes. However, it is worth noting that the existing hand gesture database could not meet the needs of gesture interaction in various fields. The cost of large-scale gesture sample extraction artificially in each field is so high that it would limit the application of gesture recognition [7; 8]. Meanwhile, the traditional gradient-based networks also require extensive iterative training to complete the model optimization. When encountering the new data, the models need torelevant their hyper-parameters to adequately incorporate the new information without catastrophic interference [9], which is inefficient. The existing networks fail to complete the optimization of the model with small size training samples, while one-shot learning could infer results as expected [10]. Therefore, the method of one-shot learning can be used to solve the problem that the model could not be optimized by the insufficient samples of skeleton-based dynamic hand gestures. However, if the current algorithm of \"one-shot learning\" is directly applied to the hand gesture recognition, there will be three gradient-based optimization problems. Firstly, due to the small amount of data, many advanced and mature algorithms, such as Momentum [11] and Adagrad [12], cannot be optimized in limited iterations; especially when encountering non-convex problems, many hyper-parameters cannot achieve convergence. Secondly, for different tasks, the parameters of the network need to be initialized randomly. If the amount of data is too small, the final model cannot achieve convergence. This can be alleviated by conducting transferring learning methods, such as fine-tuning [13; 14]. Finally, for the traditional neural network, its memory storage is limited. Additionally, the process of learning a new set of patterns will suddenly and completely erase a network's knowledge of what it had already learned, which is referred to as catastrophic interference [15]. Therefore, we need to find a memory module that can be used for large-scale storage and can also be accessed for relevant information. The large capacity enhanced memory neural networks, such as a neural Turing machine (NTM) [16], provides a feasible method for one-shot learning combined with hand gesture recognition. The NTM provides the capability to quickly encode and retrieve new information by limiting the changes in the output of the network before and after the network update [15; 17]. In addition, it can also eliminate gradient-based optimization problems. On this basis, [PERSON] [9] introduced a new and pure content-based method for accessing an external memory, which is different from previous methods, additionally using a memory location-based focusing mechanism. The method can rapidly bind never-before-seen information to the external memory after a single presentation and combines the gradient descent to slowly learn an abstract method for obtaining useful representations of raw data. As a result, it can accurately identify the categories of data that have occurred only once.