Back to articles
Articles
Volume: 30 | Article ID: art00013
Image
Efficient Preprocessing and Feature Extraction for Robust Face Recognition
  DOI :  10.2352/ISSN.2470-1173.2018.2.VIPC-252  Published OnlineJanuary 2018
Abstract

Face recognition in real world environments is mainly affected by critical factors such as illumination variation, occlusion and small sample size. This paper proposes a robust preprocessing chain and robust feature extraction in order to handle these issues simultaneously. The proposed preprocessing chain exploits Difference of Gaussian (DoG) filtering as a bandpass filter to reduce the effects of aliasing, noise and shadows, and then exploits the gradient domain as an illumination insensitive measure. On the other hand, Linear Discriminant Analysis (LDA) is one of the most successful facial feature extraction techniques, but the recognition performance of LDA is dramatically decreased by the presence of occlusion and small sample size (SSS) problem. Therefore, it is necessary to develop a robust LDA algorithm in order to handle these cases. At this point, we propose to combine Robust Sparse Principal Component Analysis (RSPCA) and LDA (RSPCA+LDA). The RSPCA is performed first in order to reduce the dimension and to deal with outliers typically affecting sample images due to pixels that are corrupted by noise or occlusion. Then, LDA in low-dimensional subspaces can operate more effectively. Experimental results on three standard databases, namely, Extended Yale-B, AR and JAFFE confirm the effectiveness of the proposed method and the results are superior to well-known methods in the literature.

Subject Areas :
Views 12
Downloads 1
 articleview.views 12
 articleview.downloads 1
  Cite this article 

Huda M.S. Algharib, O. Serdar Gedik, "Efficient Preprocessing and Feature Extraction for Robust Face Recognitionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Visual Information Processing and Communication IX,  2018,  pp 252-1 - 252-7,  https://doi.org/10.2352/ISSN.2470-1173.2018.2.VIPC-252

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2018
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology