2017/11/06 by Jiajun Zhang, Zhang, Jiajun, Zhiguo Shi +1
Computer Science · Engineering · #Advanced SAR Imaging Techniques #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Wireless Signal Modulation Classification
paper · pdf · doi:10.48550/arxiv.1711.01968
openalex publication_date 2017/11/06 · openalex created_date 2017/11/17 · openalex updated_date 2026/07/28
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%.