Abstract
Gait recognition has emerged as a powerful tool for unobtrusive and long-range identity analysis, with growing relevance in surveillance and monitoring applications. Although recent advances in deep learning and large-scale datasets have enabled highly accurate recognition under closed-set conditions, real-world deployment demands open-set gait enrollment, which means determining whether a new gait sample corresponds to a known identity or represents a previously unseen individual. In this work, we introduce a transformer-based framework for open-set gait enrollment that is both dataset-agnostic and recognition-architecture-agnostic. Our method leverages a SetTransformer to make enrollment decisions based on the embedding of a probe sample and a context set drawn from the gallery, without requiring task-specific thresholds or retraining for new environments. By decoupling enrollment from the main recognition pipeline, our model is generalized across different datasets, gallery sizes, and identity distributions. We propose an evaluation protocol that uses existing datasets in different ratios of identities and walks per identity. We instantiate our method using skeleton-based gait representations and evaluate it on two benchmark datasets (CASIA-B and PsyMo), using embeddings from three state-of-the-art recognition models (GaitGraph, GaitFormer, and GaitPT). We show that our method is flexible, is able to accurately perform enrollment in different scenarios, and scales better with data compared to traditional approaches. We will make the code and dataset scenarios publicly available.
Abstract (translated)
步态识别作为一种强有力的非侵入性和远距离身份分析工具，已在监控和监测应用中展现出日益重要的作用。尽管深度学习的最新进展以及大规模数据集的应用已经使得在闭合集合条件下实现高度准确的身份识别成为可能，但实际部署需要开放集合步态登记，这意味着必须确定一个新的步态样本是否对应于已知的身份或是之前未见过的个体。 在这项工作中，我们引入了一个基于变换器（Transformer）的框架，用于无数据集和身份认证架构依赖性的开放集合步态注册。我们的方法利用了SetTransformer，通过将探针样本和从图库中抽取的上下文集进行嵌入来做出注册决定，并且无需特定任务的阈值或重新训练以适应新环境。通过将注册过程与主要的身份识别管道解耦，我们的模型在不同的数据集、图库大小和身份分布之间实现了泛化。 我们提出了一种评估协议，利用现有数据集中不同比例的身份和每个身份的不同步态数量进行评价。我们将方法应用到基于骨架的步态表示，并在两个基准数据集（CASIA-B 和 PsyMo）上进行了测试，使用了三种最先进的识别模型（GaitGraph、GaitFormer 和 GaitPT）的嵌入。我们展示了该方法具有灵活性，在不同场景中能够准确执行注册操作，并且与传统方法相比，随着数据量的增长表现得更好。 我们将公开提供代码和数据集情景以供社区使用。
URL
https://arxiv.org/abs/2505.02815
