Gaitgraph: Graph Convolutional Network for Skeleton-Based Gait Recognition

Teepe, Torben; Khan, Ali Raza; Gilg, Johannes; Herzog, Fabian; Hörmann, Stefan; Rigoll, Gerhard

doi:10.1109/icip42928.2021.9506717

Cited by 186 publications

(110 citation statements)

References 20 publications

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“…identifying individuals from the generally similar dance motion. Thus, we employ a gait recognition algorithm, GaitGraph [26], to capture the ID information and evaluate whether it transfers correctly in synthesis motion. In gait recognition, the test set is usually split into gallery and probe set, where the identities of sequences in former set are considered known while the latter are unknown.…”

Section: Identity Scorementioning

confidence: 99%

Human Identity-Preserved Motion Retargeting in Video Synthesis by Feature Disentanglement

Ma¹,

Yu²

2022

Preprint

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Most motion retargeting methods in human action video synthesis decompose the input video to motion (dynamic information) and shape (static information). However, we observe if the dynamic information is directly transferred to another subject, it will result in unnatural synthesised motion. This phenomenon is mainly caused by neglecting subject-dependent information in motion. To solve the problem, we propose a novel motion retargeting method which can combine both subject-independent (common motion content) information from a source video and subject-dependent (individualized identity motion) information from a target video. So it can synthesize videos with a much natural appearance along with identity-preserved motion (Figure 1). In the proposed method two encoders are employed to extract identity and motion content representations respectively. We employ the adaptive instance normalization (AdaIN) layer in the generator and the instance normalization (IN) layer in the motion content encoder to synthesize the new motion. Besides, we also collected a dataset, named 𝐶ℎ𝑢𝑎𝑛𝑔101, with 101 subjects in total. Each subject performs identical dancing movement, and so it is convenient for feature disentanglement among motion and identity of each subject. Furthermore, an efficient quantitative metric for identify information is designed by gait recognition. The experiments show the proposed method can synthesize videos more naturally when the subject's identity is preserved.

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Section: Identity Scorementioning

confidence: 99%

Human Identity-Preserved Motion Retargeting in Video Synthesis by Feature Disentanglement

Ma¹,

Yu²

2022

Preprint

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“…2) GaitGraph [38] is a recent model-based gait recognition method. It models the 2D skeleton as a graph and adopts a Graph Convolution Network, i.e., the Res-GCN [33], to learn features by the contrastive loss.…”

Section: Model-based Approachesmentioning

confidence: 99%

Gait Recognition in the Wild with Dense 3D Representations and A Benchmark

Zheng¹,

Liu²,

Liu³

et al. 2022

Preprint

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Existing studies for gait recognition are dominated by 2D representations like the silhouette or skeleton of the human body in constrained scenes. However, humans live and walk in the unconstrained 3D space, so projecting the 3D human body onto the 2D plane will discard a lot of crucial information like the viewpoint, shape, and dynamics for gait recognition. Therefore, this paper aims to explore dense 3D representations for gait recognition in the wild, which is a practical yet neglected problem.In particular, we propose a novel framework to explore the 3D Skinned Multi-Person Linear (SMPL) model of the human body for gait recognition, named SMPLGait. Our framework has two elaborately-designed branches of which one extracts appearance features from silhouettes, the other learns knowledge of 3D viewpoints and shapes from the 3D SMPL model. In addition, due to the lack of suitable datasets, we build the first large-scale 3D representationbased gait recognition dataset, named Gait3D. It contains 4,000 subjects and over 25,000 sequences extracted from 39 cameras in an unconstrained indoor scene. More importantly, it provides 3D SMPL models recovered from video frames which can provide dense 3D information of body shape, viewpoint, and dynamics. Based on Gait3D, we comprehensively compare our method with existing gait recognition approaches, which reflects the superior performance of our framework and the potential of 3D representations for gait recognition in the wild. The code and dataset are available at https://gait3d.github.io.

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“…Joint positions extracted using HRNet. The bone configurations are adopted from [3] and N denotes the number of frames in the gait video. In order to perform graph convolution, the input video needs to be represented using graph, i.e., vertices and edges.…”

Section: B Pose Extractionmentioning

confidence: 99%

HEATGait: Hop-Extracted Adjacency Technique in Graph Convolution based Gait Recognition

Hasan,

Ahmed,

Kabir

2022

Preprint

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Biometric authentication using gait has become a promising field due to its unobtrusive nature. Recent approaches in model-based gait recognition techniques utilize spatio-temporal graphs for the elegant extraction of gait features. However, existing methods often rely on multi-scale operators for extracting long-range relationships among joints resulting in biased weighting. In this paper, we present HEATGait, a gait recognition system that improves the existing multi-scale graph convolution by efficient hop-extraction technique to alleviate the issue. Combined with preprocessing and augmentation techniques, we propose a powerful feature extractor that utilizes ResGCN to achieve stateof-the-art performance in model-based gait recognition on the CASIA-B gait dataset.

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Gaitgraph: Graph Convolutional Network for Skeleton-Based Gait Recognition

Cited by 186 publications

References 20 publications

Human Identity-Preserved Motion Retargeting in Video Synthesis by Feature Disentanglement

Human Identity-Preserved Motion Retargeting in Video Synthesis by Feature Disentanglement

Gait Recognition in the Wild with Dense 3D Representations and A Benchmark

HEATGait: Hop-Extracted Adjacency Technique in Graph Convolution based Gait Recognition

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