A Recognition Method of Hand Gesture Based on Stacked Denoising Autoencoder
In order to avoid the complex preprocessing, this paper proposes a recognition method based on stacked denoising autoencoder (SDAE), in which the structure and the strategies including the number of hidden units, the number of hidden layers, the level of noise and the regularization are carefully considered and analyzed for American Sign Language Dataset (ASL). Specifically, with the increasing number of hidden units and hidden layers, the optimal structure of SDAE is gradually determined, whose performance is simply measured by the recognition accuracy on testing samples. And then, the influences of the noisy strength and the regularization methods on the performance of the designed SDAE are analyzed and compared. Finally, an effective SDAE network is suggested for ASL Dataset. Experiment results show that, compared with stacked autoencoder (AE), deep belief network (DBN) and convolutional neural network (CNN) etc., the designed SDAE shows a better performance, the accuracy in ASL Dataset is up to 98.07% while the training time is reduced to 1 h.
KeywordsGesture recognition Stacked denoising autoencoder Network structure Regularization
This work is supported by National Natural Science Foundation of China under grants 61501286, 61501287, 61601274 and 61877038, the Natural Science Basic Research Plan in Shaanxi Province of China (2018JM6068), the Fundamental Research Funds for the Central Universities of Shaanxi Normal University (GK201703054 and GK201703058) and The Key Science and Technology Innovation Team in Shaanxi Province of China (2014KTC-18).
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