  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.
