Skeleton-based gait recognizers excel at modeling spatial configurations but often underuse explicit motion dynamics that are crucial under appearance changes. We introduce a plug-and-play Wavelet Feature Stream that augments any skeleton backbone with time-frequency dynamics of joint velocities. Concretely, per-joint velocity sequences are transformed by the continuous wavelet transform (CWT) into multi-scale scalograms, from which a lightweight multi-scale CNN learns discriminative dynamic cues. The resulting descriptor is fused with the backbone representation for classification, requiring no changes to the backbone architecture or additional supervision. Across CASIA-B, the proposed stream delivers consistent gains on strong skeleton backbones (e.g., GaitMixer, GaitFormer, GaitGraph) and establishes a new skeleton-based state of the art when attached to GaitMixer. The improvements are especially pronounced under covariate shifts such as carrying bags (BG) and wearing coats (CL), highlighting the complementarity of explicit time-frequency modeling and standard spatio-temporal encoders.
Figures & tables
Figure 1: Overview of the proposed plug-and-play Wavelet Feature Stream attached to a skeleton backbone. Per-joint velocities → CWT scalograms → multi-scale CNN → fusion with backbone features.
Figure 2: Wavelet Feature Stream. Given per-joint velocities from skeleton sequences, we apply CWT to obtain time–frequency maps (scalograms) and learn multi-scale dynamics via a lightweight CNN. The resulting descriptor is fused with a backbone feature for classification.
Type
Method
NM
BG
CL
Mean
Appearance- based
GaitNet [ 12 ]
91.6
85.7
58.9
78.7
GaitSet [ 2 ]
95.0
87.2
70.4
84.2
GaitPart [ 3 ]
96.2
91.5
78.7
88.8
3DLocal [ 5 ]
98.3
95.5
84.5
92.8
Skeleton- based
PoseGait [ 7 ]
68.7
44.5
36.0
49.7
GaitGraph [ 15 ]
87.7
74.8
66.3
76.3
Table 1: Comparison with state-of-the-art methods on CASIA-B (Rank-1, %). Results are reported under NM, BG, and CL conditions; Mean averages the three.
Gallery NM#1–4
Method
0 ∘
18 ∘
36 ∘
54 ∘
72 ∘
90 ∘
108 ∘
126 ∘
144 ∘
162 ∘
180 ∘
Mean
NM#5–6
GaitGraph [ 15 ]
85.3
88.5
91.0
92.5
87.2
86.5
88.4
89.2
87.9
85.9
81.9
87.7
+ Ours
88.0
91.4
93.0
92.3
92.0
88.7
89.0
89.9
92.0
90.2
85.3
90.2
GaitFormer [ 8 ]
90.9
91.2
93.7
91.9
91.9
92.7
93.3
91.8
92.5
90.5
85.5
91.5
+ Ours
92.0
91.8
94.2
94.2
93.0
94.5
94.3
93.4
93.6
91.8
86.7
92.7
GaitMixer [ 8 ]
94.4
94.9
94.6
96.3
95.3
96.3
95.3
94.7
95.3
94.7
92.2
94.9
+ Ours
94.4
94.4
95.4
96.3
95.6
96.6
95.8
95.4
95.0
93.6
93.7
95.1
Table 2: Averaged Rank-1 accuracies (%) with the wavelet stream on CASIA-B per probe angle (identical-view excluded). Bold indicates the higher score within each backbone pair (baseline vs. + Ours). Underline indicates the best score within each condition block (NM/BG/CL).
Wavelet
NM
BG
CL
Mean
Mexican Hat ( mexh )
94.7
86.3
85.7
88.8
Gaussian ( gaus1 )
95.1
86.4
84.9
88.8
Shannon ( shan )
94.9
86.1
84.4
88.5
Morlet ( morl )
95.1
87.2
86.7
89.7
Table 3: Ablation on the mother wavelet for CWT on CASIA-B (Rank-1, %).
3D skeleton-based gait emotion recognition faces high annotation costs, data scarcity, and poor generalization on heterogeneous data. This paper proposes SV-GCN, a single-stream multi-feature fusion framework with temporal invariance. We introduce intra-frame relative motion features to eliminate frame-rate sensitivity and embed heterogeneous cues at shallow layers, enabling early fusion without multi-stream complexity. For variable-length sequences, we design a global mask-guided valid-frame spatio-temporal graph convolution module, introducing frame-rate insensitivity for the first time in this domain. On the E-Gait dataset, our method achieves performance comparable to state-of-the-art while demonstrating strong generalization across varying sequence lengths and frame rates, offering a viable pathway for pre-training on large-scale skeleton-based action recognition datasets.
Shirong Lyu, Silu Quan, Yixuan Ding +1
College of Computer and Information Science, Southwest University, Chongqing, China · College of Physical Science and Technology, Southwest University, Chongqing, China · Wisesoft Inc., Chengdu, China
Gait recognition has emerged as a compelling biometric modality for surveillance and security applications, offering inherent advantages such as non-intrusiveness, resistance to disguise, and long-range identification capability. However, prevailing approaches struggle to comprehensively capture and exploit the rich biometric cues embedded in human locomotion, particularly under covariate interference including viewpoint variation, clothing change, and carrying conditions. In this paper, we present a high-precision gait recognition framework that deeply extracts and synergistically fuses gait dynamics with body shape characteristics through a multi-branch architecture grounded in deep residual learning. Specifically, we first employ the High-Resolution Network (HRNet) to perform robust skeletal keypoint estimation, preserving fine-grained spatial information even under low-resolution inputs. We then construct three complementary feature branches -- body proportion, gait velocity, and skeletal motion -- from the extracted pose sequences. A 50-layer Residual Network (ResNet-50) backbone is leveraged within a deep feature extraction module to capture hierarchically rich and discriminative representations. To effectively integrate heterogeneous feature streams, we design a Multi-Branch Feature Fusion (MFF) module inspired by channel-wise attention mechanisms, which dynamically allocates contribution weights across branches through learned activation parameters. Extensive experiments on the cross-view multi-condition CASIA-B benchmark demonstrate that our method achieves a Rank-1 accuracy of 94.52% under normal walking, with the best recognition performance among skeleton-based methods for the coat-wearing condition.
Yabo Luo, Xiaoyun Wang, Cunrong Li
School of Mechanical and Electrical Engineering, Osh State University
Gait recognition enables non-intrusive, privacy-preserving identification but suffers in uncontrolled environments due to illumination and motion sensitivity of conventional cameras. In this work, we explore gait recognition using event cameras, which offer microsecond temporal resolution and high dynamic range, naturally capturing robust dynamic cues and suppressing static noise. Existing event-based approaches typically aggregate event streams into event images over long time windows, thereby discarding fine-grained motion dynamics critical for gait recognition. Therefore, we propose EventGait, an end-to-end dual-stream framework that separately models motion and shape while preserving the advantages of events. Our dynamic stream leverages a Mixture of Spiking Experts (MoSE) with diverse neuron constants for robust dynamic perception across complex motion and illumination scenes, while the static stream learns dense shape representations via Cross-modal Structure Alignment (CroSA) with large vision foundation models. To address the absence of large-scale event-based gait datasets, we introduce a synthesis pipeline and release two new benchmarks: SUSTech1K-E and CCGR-Mini-E. Extensive experiments have shown that event-based gait recognition not only achieves results comparable to camera-based gait recognition under normal conditions but also significantly outperforms it in low-light scenarios. Our approach sets a new state of the art on both synthesized and real-world event-based gait benchmarks, highlighting the robustness and potential of event-driven gait analysis. The code and datasets are released at https://github.com/QUEAHREN/EventGait.
Senyan Xu, Shuai Chen, Chuanfu Shen +4
University of Science and Technology of China · University of Electronic Science and Technology of China