Noisy Label Learning for Gait Recognition in the Wild

Gait recognition, as a biometric technology with great potential, has been widely applied in numerous fields due to its unique advantages. Through in-depth research and the creation of in-the-wild gait datasets, gait recognition technology is progressively extending from laboratory settings to complex real-world scenarios, achieving notable advancements. However, the complexity of annotating gait data inevitably leads to labeling errors, known as noisy labels, which are one of the reasons for the suboptimal performance of in-the-wild gait recognition. To address these issues, this paper explores noisy label learning for in-the-wild gait recognition for the first time. We propose a plug-and-play gait recognition framework named Dynamic Noise Label Correction Network (DNLC). Specifically, it consists of two main parts: the dynamic class-center feature library and the label correction module, which can automatically identify and correct noisy labels based on the class-center feature library. In addition, we introduce the two-stage augmentation strategy to increase the diversity of the data and help reduce the impact of noisy labels. We integrated our proposed framework into five existing gait models and conducted extensive experiments on two widely used gait recognition datasets: Gait3D and CCPG. The results show that our framework increased the average Rank-1 accuracy of five methods by 10.03% and 6.45% on the Gait3D and CCPG datasets, respectively. These findings demonstrate the superior performance of our method.