Deep Fisher discriminant learning for mobile hand gesture recognition

作者：

Highlights：

• We collect a large mobile gesture database using an Andriod Huawei device, which is the largest database in published studies for mobile gesture recongnition systems.

• We incorporate Fisher criterion into BiLSTM network and propose F-BiLSTM and F-BiGRU to improve the traditional softmax loss training function.

• Extensive experiments on our MGD, BUAA Mobile Gesture database, and a public database are conducted to verify the superior performance of the proposed networks.

摘要

•We collect a large mobile gesture database using an Andriod Huawei device, which is the largest database in published studies for mobile gesture recongnition systems.•We incorporate Fisher criterion into BiLSTM network and propose F-BiLSTM and F-BiGRU to improve the traditional softmax loss training function.•Extensive experiments on our MGD, BUAA Mobile Gesture database, and a public database are conducted to verify the superior performance of the proposed networks.

论文关键词：Fisher discriminant,Hand gesture recognition,Mobile devices

论文评审过程：Received 4 July 2017, Revised 9 December 2017, Accepted 30 December 2017, Available online 4 January 2018, Version of Record 6 February 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.12.023