Gait recognition aims to identify a person at a distance, serving as a promising solution for long-distance and less-cooperation pedestrian recognition. Recently, significant advances in gait recognition have achieved inspiring success in many challenging scenarios by utilizing deep learning techniques. Against the backdrop that deep gait recognition has achieved almost perfect performance in laboratory datasets, much recent research has introduced new challenges for gait recognition, including robust deep representation modeling, in-the-wild gait recognition, and even recognition from new visual sensors such as infrared and depth cameras. Meanwhile, the increasing performance of gait recognition might also reveal concerns about biometrics security and privacy prevention for society. We provide a comprehensive survey on recent literature using deep learning and a discussion on the privacy and security of gait biometrics. This survey reviews the existing deep gait recognition methods through a novel view based on our proposed taxonomy. The proposed taxonomy differs from the conventional taxonomy of categorizing available gait recognition methods into the model- or appearance-based methods, while our taxonomic hierarchy considers deep gait recognition from two perspectives: deep representation learning and deep network architectures, illustrating the current approaches from both micro and macro levels. We also include up-to-date reviews of datasets and performance evaluations on diverse scenarios. Finally, we introduce privacy and security concerns on gait biometrics and discuss outstanding challenges and potential directions for future research.

Gait depicts individuals’ unique and distinguishing walking patterns and has become one of the most promising biometric features for human identification. As a fine-grained recognition task, gait recognition is easily affected by many factors and usually requires a large amount of completely annotated data that is costly and insatiable. This paper proposes a large-scale self-supervised benchmark for gait recognition with contrastive learning, aiming to learn the general gait representation from massive unlabelled walking videos for practical applications via offering informative walking priors and diverse real-world variations. Specifically, we collect a large-scale unlabelled gait dataset GaitLU-1M consisting of 1.02M walking sequences and propose a conceptually simple yet empirically powerful baseline model GaitSSB. Experimentally, we evaluate the pre-trained model on four widely-used gait benchmarks, CASIA-B, OU-MVLP, GREW and Gait3D with or without transfer learning. The unsupervised results are comparable to or even better than the early model-based and GEI-based methods. After transfer learning, GaitSSB outperforms existing methods by a large margin in most cases, and also showcases the superior generalization capacity. Further experiments indicate that the pre-training can save about 50% and 80% annotation costs of GREW and Gait3D. Theoretically, we discuss the critical issues for gait-specific contrastive framework and present some insights for further study. As far as we know, GaitLU-1M is the first large-scale unlabelled gait dataset, and GaitSSB is the first method that achieves remarkable unsupervised results on the aforementioned benchmarks.

Gait recognition aims at identifying individuals by unique walking patterns at a long distance. However, prevailing methods suffer from a large degradation when applied to large-scale surveillance systems. We find a significant cause of this issue is that previous methods heavily rely on full-view person annotations to reduce view differences by pulling closer the anchor to positive samples from different viewpoints. But, subjects under in-the-wild scenarios usually have only a limited number of sequences from different viewpoints. As a result, the available viewpoints of each subject are sparse compared to the whole dataset, and simply minimizing intra-identity differences cannot well reducing the view differences in the whole dataset. In this work, we formulate this overlooked problem as Sparse-View Gait Recognition and provide a comprehensive analysis of it by a Structural Causal Model for causalities among latent features, view distribution, and labels. Based on our analysis, we propose a simple yet effective method that enables networks to learn a more robust representation among different views. Specifically, our method consists of two parts: 1) an effective metric learning algorithmic implementation based on the backdoor adjustment, which improves the consistency of representations among different views; 2) an unsupervised view cluster algorithm to discover and identify the most influential view contexts. We evaluate the effectiveness of our method on popular GREW, Gait3D, CASIA-B, and OU-MVLP, showing that our method consistently outperforms baselines and achieves state-of-the-art performance. The code will be available at https://github.com/wj1tr0y/GaitCSV.

Cross-covariate gait recognition aims to analyze a pedestrian’s gait to extract an identity representation that is invariant across varying covariates. However, prevailing methods that have achieved good results on controlled in-the-lab datasets often perform poorly on realistic datasets. In this work, we find a significant cause is that the widely used pairwise metric learning paradigm cannot correctly handle the relationship between samples from different covariate conditions. Even worse, it may yield harmful signals that inadvertently mislead models to focus on covariate-related features, particularly when covariate distributions vary across subjects. To address this issue, we propose a Cross-Covariate Causal Intervention (GaitC³I) framework, a unified causality-inspired approach aimed at enhancing the robustness of gait recognition across diverse conditions. Specifically, our method consists of two parts: (i) an effective causal intervention metric learning paradigm based on backdoor adjustment, which strategically mitigates spurious correlations induced by covariates, thus ensuring a more invariant gait representation; and (ii) an annotation-free selection strategy that progressively matches each positive sample with negative samples from similar covariate conditions at various granularities. We demonstrate the effectiveness of our GaitC³I through extensive evaluation on six popular gait datasets–Gait3D, GREW, OUMVLP, CASIA-B, CCPG, and CCGR–achieving substantial improvements. Our method not only outperforms existing state-of-the-art models but also provides a systematic solution to remove the spurious correlations in gait recognition.

In the field of gait recognition, regarding gait as a set has emerged as a seminal approach, notably eliminating the dependence on template-based input. Although set-based methods offer notable advantages, such as insensitivity to frame order permutations and robustness to varying frame counts, their performance has consistently lagged behind that of sequence-based methods in subsequent studies. In this work, we advocate for treating gait as an unordered set and argue that the lack of set context aggregation in frame-level feature extraction is the primary limitation hindering the full potential of set-based gait recognition. To substantiate this claim, we develop a gait-oriented self-attention module and introduce a Gating Mechanism that facilitates set context awareness for each silhouette while preserving the permutation-invariant property. Specifically, the context aggregation operates on diverse bins of feature maps, interleaving fine-grained shape and motion details in an almost parameter-free manner. The Gating Mechanism is employed to ensure that frame-level features are not overwhelmed by the aggregated context. Furthermore, the sampling strategy is carefully enhanced to better support set context modeling. Our research demonstrates that set-based gait recognition can achieve state-of-the-art accuracy on in-the-wild benchmarks (77.6% on Gait3D and 81.1% on GREW) while retaining its inherent advantages.