Now You See Them, Now You Dont FusionGhost Gait for Occlusion-Robust Gait Recognition with ArabGait dataset
Seba Al Mokdad, Tamer Shanableh, Reham Mohamed Aburas
Abstract
Skeleton-based gait recognition is generally more robust to appearance changes than pixel-based methods, but this robustness depends on stable pose extraction. Clothing variation in gait recognition includes changes in garments, silhouette, and carried items; under Gulf clothing (Kandoora/Abaya), the dominant failure mode shifts from appearance change to lower-limb pose degradation caused by severe occlusion. To study this regime, we introduce ArabGait, a 45-subject indoor multi-view dataset with four walking conditions across three viewpoints, and show that Gulf clothing lowers mean lower-limb confidence from 0.811 to 0.683 and raises lower-limb failure rate from 7.98% to 17.82%, with ankles most affected. We propose FusionGhost-Gait, a fused spatiotemporal architecture that integrates spatial graph blocks and temporal modeling in one residual unit, removes redundant channel projections, and uses compact temporal Ghost kernels as a lightweight regularization mechanism for pose-noisy trajectories while preserving efficient temporal downsampling. On ArabGait cross-clothing evaluation, including Protocol A and Protocol B, FusionGhost-Gait reaches up to 67.91% Rank-1 while remaining compact (108,970- 133,674 parameters and 0.035-0.052 billion floating-point operations (GFLOPs)), achieving more than 65% fewer parameters and 75% lower FLOPs than existing baselines. These results indicate that the proposed model generalizes effectively to challenging Gulf-clothing conditions while remaining lightweight and deployable for surveillance and access-control biometric systems.