Journal ArticleDOI
View-Invariant Discriminative Projection for Multi-View Gait-Based Human Identification
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TLDR
It is suggested that it is possible to construct a gait-based identification system for arbitrary probe views, by incorporating the information of gallery data with sufficient viewing angles, and ViDP performs even better than the state-of-the-art view transformation methods.Abstract:
Existing methods for multi-view gait-based identification mainly focus on transforming the features of one view to the features of another view, which is technically sound but has limited practical utility. In this paper, we propose a view-invariant discriminative projection (ViDP) method, to improve the discriminative ability of multi-view gait features by a unitary linear projection. It is implemented by iteratively learning the low dimensional geometry and finding the optimal projection according to the geometry. By virtue of ViDP, the multi-view gait features can be directly matched without knowing or estimating the viewing angles. The ViDP feature projected from gait energy image achieves promising performance in the experiments of multi-view gait-based identification. We suggest that it is possible to construct a gait-based identification system for arbitrary probe views, by incorporating the information of gallery data with sufficient viewing angles. In addition, ViDP performs even better than the state-of-the-art view transformation methods, which are trained for the combination of gallery and probe viewing angles in every evaluation.read more
Citations
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Journal ArticleDOI
A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs
TL;DR: Experimental results show that this first work based on deep CNNs for gait recognition in the literature outperforms the previous state-of-the-art methods by a significant margin, and shows great potential for practical applications.
Journal ArticleDOI
GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition
TL;DR: GaitSet as discussed by the authors proposes a new network named GaitSet to learn identity information from the set of independent frames, which is immune to permutation of frames, and can naturally integrate frames from different videos which have been filmed under different scenarios, such as diverse viewing angles, different clothes/carrying conditions.
Journal ArticleDOI
Multi-Task GANs for View-Specific Feature Learning in Gait Recognition
TL;DR: A new multi-channel gait template, called period energy image (PEI), and multi-task generative adversarial networks (MGANs), which can leverage adversarial training to extract more discriminative features from gait sequences.
Proceedings ArticleDOI
Gait Recognition via Disentangled Representation Learning
TL;DR: In this article, a novel AutoEncoder framework is proposed to explicitly disentangle pose and appearance features from RGB imagery and the LSTM-based integration of pose features over time produces the gait feature.
Proceedings ArticleDOI
GaitGAN: Invariant Gait Feature Extraction Using Generative Adversarial Networks
TL;DR: GaitGAN is the first gait recognition method based on GAN with encouraging results and differs from the traditional GAN which has only one discriminator in that GaitGAN contains two discriminator which ensures that the generated gait images contain human identification information.
References
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Proceedings Article
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Mikhail Belkin,Partha Niyogi +1 more
TL;DR: The algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering.
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Xiaofei He,Partha Niyogi +1 more
TL;DR: These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold.
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Individual recognition using gait energy image
TL;DR: Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.
Journal ArticleDOI
Silhouette analysis-based gait recognition for human identification
TL;DR: A simple but efficient gait recognition algorithm using spatial-temporal silhouette analysis is proposed that implicitly captures the structural and transitional characteristics of gait.
Journal ArticleDOI
The humanID gait challenge problem: data sets, performance, and analysis
TL;DR: The humanlD gait challenge problem is introduced, to provide a means for measuring progress and characterizing the properties of gait recognition, and represents a radical departure from traditional computer vision research methodology.
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