Distillation-guided Representation Learning for Unconstrained Video Human Authentication

Human authentication is an important and challenging biometric task, particularly from unconstrained videos. While body recognition is a popular approach, gait recognition holds the promise of robustly identifying subjects based on walking patterns instead of appearance information. Previous gait-based approaches have performed well for curated indoor scenes; however, they tend to underperform in unconstrained situations. To address these challenges, we propose a framework, termed Holistic GAit DEtection and Recognition (H-GADER), for human authentication in challenging outdoor scenarios. Specifically, H-GADER leverages a Double Helical Signature to detect segments that contain human movement and builds discriminative features through a novel gait recognition method. To further enhance robustness, H-GADER encodes viewpoint information in its architecture, and distills learned representations from an auxiliary RGB recognition model; this allows H-GADER to learn from maximum amount of data at training time. At test time, H-GADER infers solely from the silhouette modality. Furthermore, we introduce a body recognition model through semantic, large-scale, self-supervised training to complement gait recognition. By conditionally fusing gait and body representations based on the presence/absence of gait information as decided by the gait detection, we demonstrate significant improvements compared to when a single modality or a naive feature ensemble is used. We evaluate our method on multiple existing State-of-The-Arts(SoTA) gait baselines and demonstrate consistent improvements on indoor and outdoor datasets, especially on the BRIAR dataset, which features unconstrained, long-distance videos, achieving a 28.9% improvement.