View-Invariant Gait Representation Using Joint Bayesian Regularized Non-Negative Matrix Factorization
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.
Cite
Text
Babaee and Rigoll. "View-Invariant Gait Representation Using Joint Bayesian Regularized Non-Negative Matrix Factorization." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.303Markdown
[Babaee and Rigoll. "View-Invariant Gait Representation Using Joint Bayesian Regularized Non-Negative Matrix Factorization." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/babaee2017iccvw-viewinvariant/) doi:10.1109/ICCVW.2017.303BibTeX
@inproceedings{babaee2017iccvw-viewinvariant,
title = {{View-Invariant Gait Representation Using Joint Bayesian Regularized Non-Negative Matrix Factorization}},
author = {Babaee, Maryam and Rigoll, Gerhard},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2017},
pages = {2583-2589},
doi = {10.1109/ICCVW.2017.303},
url = {https://mlanthology.org/iccvw/2017/babaee2017iccvw-viewinvariant/}
}