Proceedings ArticleDOI
View-Invariant Gait Representation Using Joint Bayesian Regularized Non-negative Matrix Factorization
Maryam Babaee,Gerhard Rigoll +1 more
- pp 2583-2589
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TLDR
A new method to learn low-dimensional view-invariant gait feature for person identification/verification by utilizing a function of Joint Bayesian as a regularizer coupled with the main objective function of non-negative matrix factorization to map gait features into a low- dimensional space is proposed.Abstract:
Gait as a biometric feature has been investigated for human identification and biometric application. However, gait is highly dependent on the view angle. Therefore, the proposed gait features do not perform well when a person is changing his/her orientation towards camera. To tackle this problem, we propose a new method to learn low-dimensional view-invariant gait feature for person identification/verification. We model a gait observed by several different points of view as a Gaussian distribution and then utilize a function of Joint Bayesian as a regularizer coupled with the main objective function of non-negative matrix factorization to map gait features into a low-dimensional space. This process leads to an informative gait feature that can be used in a verification task. The performed experiments on a large gait dataset confirms the strength of the proposed method.read more
Citations
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Journal ArticleDOI
Person identification from partial gait cycle using fully convolutional neural networks
TL;DR: Zhang et al. as discussed by the authors proposed a deep learning-based approach to transform incomplete Gait Energy Image (GEI) to the corresponding complete GEI obtained from a full gait cycle by training several auto encoders independently and then combining them as a uniform model.
Proceedings ArticleDOI
Gait Recognition from Incomplete Gait Cycle
TL;DR: A gait recognition algorithm from an incomplete gait cycle information is proposed by creating an incomplete Energy Image (GEI) from a few available silhouettes of a subject and reconstructing the complete GEI from incomplete GEI using a deep auto-encoder.
Proceedings ArticleDOI
Gait Energy Image Restoration Using Generative Adversarial Networks
TL;DR: This paper proposes a Generative Adversarial Network (GAN) in order to address the problem of gait recognition from incomplete gait cycle and evaluates the approach on the OULP large gait dataset confirming that the proposed architecture successfully reconstructs complete GEIs from even extreme complete gait cycles.
Journal Article
A new representation for human gait recognition : Motion silhouettes image (MSI)
Toby H. W. Lam,Raymond S. T. Lee +1 more
TL;DR: Motion Silhouettes Image (MSI) is a grey-level image which embeds the critical spatio-temporal information which has a high discriminative power for gait recognition and can also reduce the storage size of the dataset.
Journal ArticleDOI
NMF based image sequence analysis and its application in gait recognition
TL;DR: Experimental results show that this method can achieve high recognition accuracy, and has a strong adaptability in cross-view and multi-clothes conditions, which means it can better adapt to the real environment.
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