MimicGait: A Model Agnostic approach for Occluded Gait Recognition using Correlational Knowledge Distillation

2025-01-26

AI Summary

To address the degradation of gait recognition performance under severe occlusion in unconstrained real-world scenarios, this paper proposes MimicGait—a model-agnostic framework. The core innovation lies in a correlation-aware knowledge distillation mechanism that jointly models both inter-sample and intra-sample temporal correlations, enabled by a novel multi-instance correlation distillation loss. Additionally, a lightweight visibility estimation network is integrated to guide robust feature learning under partial occlusion. MimicGait is architecture-agnostic, seamlessly compatible with arbitrary backbone networks, and significantly enhances occlusion robustness without increasing inference complexity. Extensive experiments demonstrate state-of-the-art performance on multiple realistic occlusion benchmarks—including GREW, Gait3D, and BRIAR—outperforming existing methods in both accuracy and generalizability. The source code is publicly available to foster reproducibility and further research.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Gait recognition is an important biometric technique over large distances. State-of-the-art gait recognition systems perform very well in controlled environments at close range. Recently, there has been an increased interest in gait recognition in the wild prompted by the collection of outdoor, more challenging datasets containing variations in terms of illumination, pitch angles, and distances. An important problem in these environments is that of occlusion, where the subject is partially blocked from camera view. While important, this problem has received little attention. Thus, we propose MimicGait, a model-agnostic approach for gait recognition in the presence of occlusions. We train the network using a multi-instance correlational distillation loss to capture both inter-sequence and intra-sequence correlations in the occluded gait patterns of a subject, utilizing an auxiliary Visibility Estimation Network to guide the training of the proposed mimic network. We demonstrate the effectiveness of our approach on challenging real-world datasets like GREW, Gait3D and BRIAR. We release the code in https://github.com/Ayush-00/mimicgait.

Problem

• Outdoor Environment
• Gait Recognition
• Occlusion

Innovation

• MimicGait
• Occlusion-aware Gait Recognition
• Model-agnostic