Human identification based on Gait Manifold

Wang, Xiuhui; Yan, Wei Qi

doi:10.1007/s10489-022-03818-4

Cited by 4 publications

(4 citation statements)

References 33 publications

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“…The proposed model in this paper is further evaluated on the OU-ISIR-LP dataset for its generalization capabilities. The methods participating in the comparative experiment include: GEINet [38], GEI-SCNN [43], DeepGait+JB [44], C3D-SCNN [45], Nonlocal [46], GaitManifold [13], SA-HMM [8], GOFI [24], and GaitGL-HBS proposed by Zhu et al [42]. The experimental results are presented in Table 3.…”

Section: Experimental Results and Analysis On Ou-isir-lp Datasetmentioning

confidence: 99%

“…In a word, representation-based gait recognition methods do not need high-resolution gait key features and have less constraints, so they are more suitable for general gait recognition tasks. In [13], through combining the nonlinear dimensionality reduction by using gait manifold and the temporal feature of gated recurrent unit together, a new gait-based pedestrian identification framework was presented. Eddine and Dugelay [14] provided means for multimodal gait recognition, by introducing the "Event-based, RGB, and Thermal Gait" database that contains event-camera acquisition, simultaneously with conventional RGB and thermal videos.…”

Section: Related Workmentioning

confidence: 99%

“…The selected comparison methods selected include: CNN-LB proposed by Wu et al [37], Gaitset proposed by Zhang et al [5], GaitNet-2 proposed by Song et al [39], MGAN proposed by He et al [40], GaitManifold proposed by Wang and Yan [13], SA-HMM proposed by Wang et al [8], GOFI proposed by Ashwin et al [24], MMGaitFormer proposed by Cui and Kang [41], and GaitGL-HBS proposed by Zhu et al [42]. The experimental results are shown in Table 2.…”

Section: A Experimental Configurationmentioning

confidence: 99%

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Pedestrian Re-Identification Based on Gait Analysis

Shan,

Yu,

Gao

2023

IEEE Access

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Pedestrian re-identification is a crucial task in various safety applications, such as traffic management, collision avoidance, and emergency response. One of the challenging issues in pedestrian reidentification is how to identify the same object when the collection perspective changes or carrying different items. To address the above issues, this paper proposes a new gait based pedestrian re-identification method from the perspective of improving the representation ability of different pedestrians. The proposed method extracts spatial and temporal features separately from the gait data and designs a spatiotemporal feature fusion module at the top of the network to prevent features from interfering with each other in different domains. The temporal scoring strategy based on gated recurrent unit (GRU) is then used to evaluate the image quality of each frame in the network branch where temporal gait features are extracted. Finally, the proposed method is evaluated on two open datasets, CASIA-B and OU-ISIR-LP. Experimental results show that, compared with existing methods, the proposed method has a higher recognition rate under cross-view and scene conditions, such as carrying a bag and heavily clothed, in which the proposed method demonstrates better performance in pedestrian recognition. These results highlight the effectiveness and robustness of the proposed method and demonstrate its potential as a future research direction in pedestrian re-identification.INDEX TERMS Gait analysis, gait recognition, spatiotemporal feature fusion, pedestrian re-identification

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Section: Experimental Results and Analysis On Ou-isir-lp Datasetmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Experimental Configurationmentioning

confidence: 99%

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Pedestrian Re-Identification Based on Gait Analysis

Shan,

Yu,

Gao

2023

IEEE Access

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“…In order to address the aforementioned issue, previous researchers have utilized several non-linear dimensionality reduction methods, such as locally preserving projection (LPP) [25], locally linear embedding (LLE) [26], and neighborhood preserving embedding (NPE). However, although these methods have achieved non-linear dimensionality reduction, their classification capability is limited.…”

Section: Feature Extraction (Sdspca-npe)mentioning

confidence: 99%

Identity Recognition System Based on Multi-Spectral Palm Vein Image

Wu,

Li,

Zhang

et al. 2023

Electronics

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A multi-spectral palm vein image acquisition device based on an open environment has been designed to achieve a highly secure and user-friendly biometric recognition system. Furthermore, we conducted a study on a supervised discriminative sparse principal component analysis algorithm that preserves the neighborhood structure for palm vein recognition. The algorithm incorporates label information, sparse constraints, and local information for effective supervised learning. By employing a robust neighborhood selection technique, it extracts discriminative and interpretable principal component features from non-uniformly distributed multi-spectral palm vein images. The algorithm addresses challenges posed by light scattering, as well as issues related to rotation, translation, scale variation, and illumination changes during non-contact image acquisition, which can increase intra-class distance. Experimental tests are conducted using databases from the CASIA, Tongji University, and Hong Kong Polytechnic University, as well as a self-built multi-spectral palm vein dataset. The results demonstrate that the algorithm achieves the lowest equal error rates of 0.50%, 0.19%, 0.16%, and 0.1%, respectively, using the optimal projection parameters. Compared to other typical methods, the algorithm exhibits distinct advantages and holds practical value.

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