By Topic

Face Recognition Based on Sparse Representation and Joint Sparsity Model with Matrix Completion

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
$33 $13
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)
Fernando Kentaro Inaba ; Univ. Fed. do Espirito Santo (UFES), Vitoria, Brazil ; Evandro Ottoni Teatini Salles

In this paper we verify the impacts of the Joint Sparsity Model with Matrix Completion (JSM-MC) for the composition of training set in the context of face recognition using the Sparse Representation-based Classifier (SRC). A pre-processing step (histogram equalization) is performed in the face images to reduce the effects of illumination change. A clustering of training images is done to reduce the training set and uses the l1-norm of the sparse representation coefficients instead of the residuals for classification. The results are evaluated using a database with different illumination conditions and we also investigate the behavior of the system when the face image is partially occluded.

Published in:

IEEE Latin America Transactions  (Volume:10 ,  Issue: 1 )