GaitKD: A Universal Decoupled Distillation Framework for Efficient Gait Recognition

• URL: http://arxiv.org/abs/2604.26255v1
• Date: Wed, 29 Apr 2026 03:24:38 GMT
• Title: GaitKD: A Universal Decoupled Distillation Framework for Efficient Gait Recognition
• Abstract summary: Gait recognition is an attractive biometric modality for long-range and contact-free identification.<n> Knowledge distillation offers a natural way to transfer knowledge from a powerful teacher to an efficient student.<n>We propose GaitKD, a distillation framework that decouples gait knowledge transfer into two complementary components.
• Score: 51.75783918486982
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gait recognition is an attractive biometric modality for long-range and contact-free identification, but high-performing gait models often rely on deep and computationally expensive architectures that are difficult to deploy in practice. Knowledge distillation (KD) offers a natural way to transfer knowledge from a powerful teacher to an efficient student; however, standard KD is often less effective for part-structured gait models, where supervision is formed from both part-wise classification logits and part-wise retrieval embeddings. In this paper, we propose GaitKD, a distillation framework that decouples gait knowledge transfer into two complementary components: decision-level distillation and boundary-level distillation. Specifically, GaitKD aligns the teacher and student through part-calibrated logit distillation to transfer inter-class decision relations, while preserving the teacher-induced partitioning of the embedding space through an activation-boundary objective instead of direct feature regression. With a simple aligned part-wise design, GaitKD supports heterogeneous teacher-student gait models without introducing additional inference cost. Experimental results across multiple gait recognition benchmarks and teacher-student configurations show consistent improvements over strong gait baselines. Our study demonstrates that the two transfer components are complementary, and boundary-preserving distillation provides more stable performance than direct feature regression. Source code is available at https://github.com/liyiersan/GaitKD/

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