Deformable Deep Convolutional Generative Adversarial Network in Microwave Based Hand Gesture Recognition System
This addresses a specific problem for vision-based gesture recognition systems in low-light environments, with incremental improvements in performance and efficiency.
The paper tackled hand gesture recognition in dark conditions by developing a system using microwave transceivers and a new deep learning architecture, achieving a 10% improvement in recognition rate and a 30% reduction in testing time.
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 databases of hand gesture, we propose a new machine learning architecture called deformable deep convolutional generative adversarial network. Experimental results show the new architecture can upgrade the recognition rate by 10% and the deformable kernel can reduce the testing time cost by 30%.