Robust recognition of noisy and partially occluded faces using iteratively reweighted fitting of eigenfaces

Authors:
Wangmeng Zuo;Kuanquan Wang;David Zhang
Affiliations:
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong
Venue:
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Year:
2006

Citing 8
Cited 0

Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
Robust Principal Component Analysis with Adaptive Selection for Tuning Parameters

The Journal of Machine Learning Research
Overview of the Face Recognition Grand Challenge

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble

IEEE Transactions on Neural Networks
Robust principal component analysis by self-organizing rules based on statistical physics approach

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Robust recognition of noisy and partially occluded faces is essential for an automated face recognition system, but most appearance-based methods (e.g., Eigenfaces) are sensitive to these factors. In this paper, we propose to address this problem using an iteratively reweighted fitting of the Eigenfaces method (IRF-Eigenfaces). Unlike Eigenfaces fitting, in which a simple linear projection operation is used to extract the feature vector, the IRF-Eigenfaces method first defines a generalized objective function and then uses the iteratively reweighted least-squares (IRLS) fitting algorithm to extract the feature vector by minimizing the generalized objective function. Our simulated and experimental results on the AR database show that IRF-Eigenfaces is far superior to both Eigenfaces and to the local probabilistic method in recognizing noisy and partially occluded faces.