By Topic

Adaptive and fixed eigenspace methods with a novel fitness measure for video based face recognition

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

3 Author(s)
Alaa Eleyan ; Department of Electrical and Electronic Engineering, Eastern Mediterranean University ; Huseyin Ozkaramanli ; Hasan Demirel

In this paper a new system for identifying faces from video sequences using adaptive and fixed eigenspace approaches with a novel fitness measure is proposed. During the recognition process, each image in the gallery set is assigned a fitness value. The fitness value is updated for each frame and at the end of the probe video; the person corresponding to the gallery image with the highest fitness value is declared to be the identified person in the probe video. Eigenspace is used, for generating the feature vectors from the gallery set images. Two approaches have been introduced, where in the first approach; the eigenspace is updated after each frame is processed. The eigenspace update is performed by updating the fitness values and discarding the gallery images with the lowest respective fitness values. In the second method, a fixed eigenspace is generated from the initial gallery set and the fitness value for each gallery image is updated through the processing of the frames in the probe video. Again, in the end the gallery face image with the highest fitness value is declared to be the identified person. The BANCA video face database was adapted for performance testing. Both of the methods showed very competitive recognition rates in different scenarios.

Published in:

Computer and Information Sciences, 2009. ISCIS 2009. 24th International Symposium on

Date of Conference:

14-16 Sept. 2009