Jie Chen, Vishal M. Patel, Li Liu, Vili Kellokumpu, Guoying Zhao, Matti Pietikäinen, Rama Chellappa, Robust local features for remote face recognition, Image and Vision Computing, Volume 64, 2017, Pages 34-46, ISSN 0262-8856, https://doi.org/10.1016/j.imavis.2017.05.006
Robust local features for remote face recognition
|Author:||Chen, Jie1; Patel, Vishal M.2; Liu, Li1,3;|
1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
2Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA
3College of Information System and Management, National University of Defense Technology, China
4Department of Electrical and Computer Engineering, University of Maryland, College Park, USA
|Online Access:||PDF Full Text (PDF, 1.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019080523471
|Publish Date:|| 2019-08-31
In this paper, we propose a robust local descriptor for face recognition. It consists of two components, one based on a shearlet-decomposition and the other on local binary pattern (LBP). Shearlets can completely analyze the singular structures of piecewise smooth images, which is useful since singularities and irregular structures carry useful information in an underlying image. Furthermore, LBP is effective for describing the edges extracted by shearlets even when the images contain high level of noise. Experimental results using the Face Recognition Grand Challenge dataset show that the proposed local descriptor significantly outperforms many widely used features (e.g., Gabor and deep learning-based features) when the images are corrupted by random noise, demonstrating robustness to noise. In addition, experimental results show promising results for two challenging datasets which have poor image quality, i.e., a remote face dataset and the Point and Shoot Face Recognition Challenge dataset.
Image and vision computing
|Pages:||34 - 46|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
This work was supported by Academy of Finland 277395, TekesFidipro Program 1849/31/2015 and Infotech Oulu. VMP was supportedby US Office of Naval Research (ONR) Grant YIP N00014-16-1-3134.
|Academy of Finland Grant Number:||
277395 (Academy of Finland Funding decision)
© 2017 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.