Optimization of Deep Neural Network based Hand Gesture Classification Model for Large-Scale Dataset

Hand gesture classification is a key step of gesture based human-computer interaction. The hand gesture dataset with depth information is increasing faster which brings challenges for deep neural network-based hand gesture classification model, such as data loading of big gesture data, optimization of model and visualization of training stage. In this paper, an optimization method of deep neural network-based hand gesture classification model for large-scale dataset is proposed, which improves the efficiency of data loading and makes the model have better time complexity and space complexity. Experimental results show that the proposed optimization method can effectively improve the training efficiency of classification model for large gesture data sets, and make classification models easy to train in a unified framework, which provides a good foundation for the research of hand gesture classification model.