LAUSR

We present a novel learning-based static gesture recognition framework using electromagnetic (EM) scattered field data, which can efficiently address some significant issues in traditional vision-based recognition approaches. An end-to-end complex-valued attention convolutional neural network (CNN) is devised to train the gesture recognizer, wherein the attention module is designed to learn robust region-of-interest-aware features. Extensive numerical experiments are conducted on a public static hand gesture dataset. Both full and limited aperture measurements with transverse magnetic wave illumination are investigated. It is numerically shown that: first, both complex-valued convolutional and attention module contribute to the excellent performance. The recognition accuracy is above 99.0% for full aperture and even about 95.32% under the limited one-eighth aperture, respectively, and second, the proposed method not only has good scalability to the case with limited aperture, but also performs much better than previous state-of-the-art deep networks.