Advancements in sensing techniques have fueled the construction of a worldwide smart environment. An accompanying concern is the security issue. This article presents a noninvasive user authentication technique using an ultra-wideband (UWB) radar sensor. Human gait micro-Doppler signatures captured by the radar are used as the biometrics of individuals. Unlike the existing authentication techniques, our proposed method does not require a gallery set for retrieval during the testing stage. Instead, we formalize the authentication task as a one-class classification problem and utilize a generative adversarial network (GAN) to characterize the legal users’ movement modes, especially the fine-grained distinctions of micro-Doppler signatures. Meanwhile, the discriminator automatically outputs the prediction result, indicating whether a user is legal or not. The fully convolutional network (FCN) architecture and a fine-grained recognition module (FGM) are added to enhance the discrimination ability of the model. The experiments are carried out using measurement data from 15 subjects, and the results demonstrate that the proposed method achieves an equal error rate (EER) of 0.234, outperforming the comparative algorithms by at least 9.8%. Moreover, the model is evaluated for its robustness against various attacks as well as different walking styles. An ablation study is conducted to verify the effectiveness of the network design.