Existing gait recognition methods typically identify individuals based on the similarity between probe and gallery samples. However, these methods often neglect the fact that the gallery may not contain identities corresponding to the probes, leading to incorrect recognition.To identify Out-of-Gallery (OOG) gait queries, we propose an Evidence-based Match-status-Aware Gait Recognition (EMA-GR) framework. Inspired by Evidential Deep Learning (EDL), EMA-GR is designed to quantify the uncertainty associated with the match status of recognition. Thus, EMA-GR identifies whether the probe has a counterpart in the gallery. Specifically, we adopt an evidence collector to gather match status evidence from a recognition result pair and parameterize a Dirichlet distribution over the gathered evidence, following the Dempster-Shafer Theory of Evidence (DST). We measure the uncertainty and predict the match status of the recognition results, and thus determine whether the probe is an OOG query.To the best of our knowledge, our method is the first attempt to tackle OOG queries in gait recognition. Moreover, EMA-GR is agnostic against gait recognition methods and improves the robustness against OOG queries. Extensive experiments demonstrate that our method achieves state-of-the-art performance on datasets with OOG queries, and can also generalize well to other identity-retrieval tasks. Importantly, our method surpasses existing state-of-the-art methods by a substantial margin, achieving a 51.26% improvement when the OOG query rate is around 50% on OUMVLP.

翻译：现有的步态识别方法通常基于探针样本与图库样本之间的相似性来识别个体。然而，这些方法往往忽略了一个事实，即图库中可能不包含与探针对应的身份，从而导致错误识别。为了识别库外步态查询，我们提出了一种基于证据的匹配状态感知步态识别框架。受证据深度学习的启发，EMA-GR旨在量化与识别匹配状态相关的不确定性。因此，EMA-GR能够判断探针在图库中是否存在对应身份。具体而言，我们采用证据收集器从识别结果对中收集匹配状态证据，并依据Dempster-Shafer证据理论对收集到的证据参数化一个狄利克雷分布。我们通过测量不确定性并预测识别结果的匹配状态，从而判定探针是否为库外查询。据我们所知，本方法是首次尝试解决步态识别中的库外查询问题。此外，EMA-GR不依赖于特定步态识别方法，并能提升对库外查询的鲁棒性。大量实验表明，我们的方法在包含库外查询的数据集上取得了最先进的性能，并能很好地泛化至其他身份检索任务。重要的是，本方法显著超越了现有最优方法，在OUMVLP数据集上当库外查询率约为50%时，性能提升了51.26%。