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Gait recognition holds the promise of robustly identifying subjects based onwalking patterns instead of appearance information. While previous approacheshave performed well for curated indoor data, they tend to underperform inunconstrained situations, e.g. in outdoor, long distance scenes, etc. Wepropose a framework, termed GAit DEtection and Recognition (GADER), for humanauthentication in challenging outdoor scenarios. Specifically, GADER leveragesa Double Helical Signature to detect segments that contain human movement andbuilds discriminative features through a novel gait recognition method, whereonly frames containing gait information are used. To further enhancerobustness, GADER encodes viewpoint information in its architecture, anddistills representation from an auxiliary RGB recognition model, which enablesGADER to learn from silhouette and RGB data at training time. At test time,GADER only infers from the silhouette modality. We evaluate our method onmultiple State-of-The-Arts(SoTA) gait baselines and demonstrate consistentimprovements on indoor and outdoor datasets, especially with a significant25.2% improvement on unconstrained, remote gait data.

Distillation-guided Representation Learning for Unconstrained Gait Recognition