An optimized Generative Adversarial Network based continuous sign language classification

作者：

Highlights：

• Characterization of manual and non-manual gestures in recognizing the sign gestures.

• Deep Networks of self-learning capacity to achieve higher recognition rate.

• Iterative optimization on hyperparameters and considered limited training data.

• Recognize multimodal and multilingual sign corpus with multi-signer variation.

摘要

•Characterization of manual and non-manual gestures in recognizing the sign gestures.•Deep Networks of self-learning capacity to achieve higher recognition rate.•Iterative optimization on hyperparameters and considered limited training data.•Recognize multimodal and multilingual sign corpus with multi-signer variation.

论文关键词：Continuous sign language recognition,Generative Adversarial Networks,Sign classification,Feature dimensionality reduction,Hyperparameter optimization

论文评审过程：Received 5 June 2020, Revised 17 March 2021, Accepted 22 May 2021, Available online 28 May 2021, Version of Record 8 June 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115276