RPNet: Gait Recognition With Relationships Between Each Body-Parts

Qin, Hao; Chen, Zhenxue; Guo, Qingqiang; Wu, Qi; Lu, Mengxu

doi:10.1109/tcsvt.2021.3095290

Cited by 23 publications

(10 citation statements)

References 45 publications

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“…The standard of evidence admissibility in the United States also has been discussed [ 104 , 105 ] Furthermore, there are many papers that discuss the reliability of gait analysis [ 60 , 68 , 72 , 89 , 93 , 94 , 96 , 105 , 106 , 108 , 116 , 120 , 123 , 125 , 126 , 129 , 130 , 132 , 136 , 140 , 144 ].…”

Section: Gait Analysismentioning

confidence: 99%

Interpol review of forensic video analysis, 2019–2022

Geradts

Riphagen

2023

Forensic Science International: Synergy

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Section: Gait Analysismentioning

confidence: 99%

Interpol review of forensic video analysis, 2019–2022

Geradts

Riphagen

2023

Forensic Science International: Synergy

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“…The taxonomy gives an overview of how deep learning is used in different publications and for different lengths of time. Many taxonomies have been proposed in the previous review papers; however, different published papers present different perspectives, such as in [ 73 ], where authors explain the taxonomy based on the categories of sensor, covariate factor, and classifier. A feature-based taxonomy is presented in [ 74 ].…”

Section: Taxonomymentioning

confidence: 99%

“…The best performance of deep methods on the OU-MVLP until 2020 was 89.18%. Some methods [ 73 , 88 , 98 ] produced outstanding results for the gait recognition rate on OU-MVLP in 2021. Among the methods, ref.…”

Section: Trends and Performance Analysismentioning

confidence: 99%

Person Recognition Based on Deep Gait: A Survey

Khaliluzzaman

Uddin

Deb

et al. 2023

Sensors

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Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future.

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“…Moreover, spatial feature representations can be obtained from the silhouette of an individual frame, which can represent appearance characteristics, while temporal feature representations can be captured from consecutive silhouettes, in which the relationship between adjacent frames can reflect the temporal characteristics and motion patterns. Therefore, some appearance-based methods have overcome the challenges of pose estimation and achieved competitive performance [4][5][6][7][8]21,22,29]. Particularly, the first opensource gait recognition framework, named OpenGait (https://github.com/ShiqiYu/OpenGait, accessed on 29 January 2023) [18], encompassed a series of state-of-the-art appearance-based methods for gait recognition.…”

Section: Related Workmentioning

confidence: 99%

“…Gait recognition: Current deep-learning-based gait recognition methods can be broadly classified into two categories: model-based [13][14][15][16] and appearance-based [4][5][6][7][8][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Model-based methods leverage the relationships between bone joints and pose information to create models of walking patterns and human body structures [13], such as OpenPose [14], HRNet [15], and DensePose [16].…”

Section: Related Workmentioning

confidence: 99%

Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition

Guan,

Hua,

Zhao

2023

Applied Sciences

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Gait recognition is a distinctive biometric technique that can identify pedestrians by their walking patterns from considerable distances. A critical challenge in gait recognition lies in effectively acquiring discriminative spatial-temporal representations from silhouettes that exhibit invariance to disturbances. In this paper, we present a novel gait recognition network by aggregating features in the spatial-temporal and view domains, which consists of two-path spatial-temporal feature fusion module and view embedding module. Specifically, two-path spatial-temporal feature fusion module firstly utilizes multi-scale feature extraction (MSFE) to enrich the input features with multiple convolution kernels of various sizes. Then, frame-level spatial feature extraction (FLSFE) and multi-scale temporal feature extraction (MSTFE) are parallelly constructed to capture spatial and temporal gait features of different granularities and these features are fused together to obtain muti-scale spatial-temporal features. FLSFE is designed to extract both global and local gait features by employing a specially designed residual operation. Simultaneously, MSTFE is applied to adaptively interact multi-scale temporal features and produce suitable motion representations in temporal domain. Taking into account the view information, we introduce a view embedding module to reduce the impact of differing viewpoints. Through the extensive experimentation over CASIA-B and OU-MVLP datasets, the proposed method has achieved superior performance to the other state-of-the-art gait recognition approaches.

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RPNet: Gait Recognition With Relationships Between Each Body-Parts

Cited by 23 publications

References 45 publications

Interpol review of forensic video analysis, 2019–2022

Interpol review of forensic video analysis, 2019–2022

Person Recognition Based on Deep Gait: A Survey

Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition

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