用全复数卷积网络提升雷达手势识别准确率

Hand gesture recognition systems have yielded many exciting advancements in the last decade and become more popular in HCI (human-computer interaction) with several application areas, which spans from safety and security applications to automotive field. Various deep neural network architectures have already been inspected for hand gesture recognition systems, including multi-layer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN) and a cascade of the last two architectures known as CNN-RNN. However, a major problem still exists, which is most of the existing ML algorithms are designed and developed the building blocks and techniques for real-valued (RV). Researchers applied various RV techniques on the complex-valued (CV) radar images, such as converting a CV optimisation problem into a RV one, by splitting the complex numbers into their real and imaginary parts. However, the major disadvantage of this method is that the resulting algorithm will double the network dimensions. Recent work on RNNs and other fundamental theoretical analysis suggest that CV numbers have a richer representational capacity, but due to the absence of the building blocks required to design such models, the performance of CV networks are marginalised. In this report, we propose a fully CV-CNN, including all building blocks, forward and backward operations, and derivatives all in complex domain. We explore our proposed classification model on two sets of CV hand gesture radar images in comparison with the equivalent RV model. In chapter five, we propose a CV-forward residual network, for the purpose of binary classification of the two sets of CV hand gesture radar datasets and compare its performance with our proposed CV-CNN and a baseline CV-forward CNN.