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- W2964162383 abstract "Traditional vision-based hand gesture recognition systems is limited under dark circumstances. In this paper, we build a hand gesture recognition system based on microwave transceiver and deep learning algorithm. A Doppler radar sensor with dual receiving channels at 5.8GHz is used to acquire a big database of hand gestures signals. The received hand gesture signals are then processed with time-frequency analysis. Based on these big database of hand gesture, we propose a new classification architecture called deformable deep convolutional generative adversarial network. Experimental results shows the new architecture can upgrade the recognition rate by 10% and the deformable kernel can reduce the testing time cost by 30%." @default.
- W2964162383 created "2019-07-30" @default.
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- W2964162383 date "2017-10-01" @default.
- W2964162383 modified "2023-09-28" @default.
- W2964162383 title "Deformable deep convolutional generative adversarial network in microwave based hand gesture recognition system" @default.
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- W2964162383 doi "https://doi.org/10.1109/wcsp.2017.8170976" @default.
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