Gait recognition holds the promise of robustly identifying subjects based on their walking patterns instead of color information. While previous approaches have performed well for curated indoor scenes, they have significantly impeded applicability in unconstrained situations, e.g. outdoor, long distance scenes. We propose an end-to-end GAit DEtection and Recognition (GADER) algorithm for human authentication in challenging outdoor scenarios. Specifically, GADER leverages a Double Helical Signature to detect the fragment of human movement and incorporates a novel gait recognition method, which learns representations by distilling from an auxiliary RGB recognition model. At inference time, GADER only uses the silhouette modality but benefits from a more robust representation. Extensive experiments on indoor and outdoor datasets demonstrate that the proposed method outperforms the State-of-The-Arts for gait recognition and verification, with a significant 20.6% improvement on unconstrained, long distance scenes.

翻译：步态识别有望通过个体的行走模式而非颜色信息实现鲁棒的身份识别。尽管先前方法在受控室内场景中表现良好，但在无约束环境下（例如室外远距离场景）的应用受到显著制约。我们提出了一种端到端的步态检测与识别（GADER）算法，用于挑战性室外场景中的人体身份验证。具体而言，GADER采用双螺旋特征提取人体运动片段，并融合了一种新型步态识别方法——该方法通过从辅助RGB识别模型中蒸馏学习表征。在推理阶段，GADER仅使用轮廓模态即可受益于更鲁棒的表征。在室内外数据集上的大量实验表明，所提方法在步态识别与验证任务上超越现有最优技术，且在无约束远距离场景下实现了20.6%的显著提升。