View-Invariant Gait Representation Using Joint Bayesian Regularized Non-Negative Matrix Factorization

Maryam Babaee, Gerhard Rigoll; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2583-2589

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 lowdimensional 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.

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[bibtex]
@InProceedings{Babaee_2017_ICCV,
author = {Babaee, Maryam and Rigoll, Gerhard},
title = {View-Invariant Gait Representation Using Joint Bayesian Regularized Non-Negative Matrix Factorization},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}