By Topic

Face recognition based on a two-view projective transformation using one sample per subject

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kuo, C.-H. ; Dept. of Electr. Eng., Chang Gung Univ., Taoyuan, Taiwan ; Lee, J.-D.

In this study, the authors propose a novel face recognition algorithm based on geometric features to alleviate the one sample per subject problem, called the robust estimation system. Our application adopts both local and global information for robust estimation. The authors utilise the original images from the ORL and Yale databases for evaluation. The images of the FERET database are pre-processed to extract the pure face region and execute the affine transformation. The authors roughly divide the face images into four block images that are most significant for the face: left eye, right eye, nose and mouth. The feature extraction using magnitude of first-order gradients, based on geometric features, is ideal for estimating a single sample. While conducting the classification stage, local features are putatively matched before processing or the global random sample consensus robust estimation features, with the aim of identifying the fundamental matrix between two matched face images. Finally, similarity scores are calculated, and the candidate awarded the highest score is designated the correct subject. Experiments were implemented using the FERET, ORL and Yale databases to demonstrate the efficiency of the proposed method. The experimental results show that our algorithm greatly improves recognition performance compared with the existing methods.

Published in:

Computer Vision, IET  (Volume:6 ,  Issue: 5 )