By Topic

A sparse representation approach to face matching across plastic surgery

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

4 Author(s)
Aggarwal, G. ; Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA ; Biswas, S. ; Flynn, P.J. ; Bowyer, K.W.

Plastic surgery procedures can significantly alter facial appearance, thereby posing a serious challenge even to the state-of-the-art face matching algorithms. In this paper, we propose a novel approach to address the challenges involved in automatic matching of faces across plastic surgery variations. In the proposed formulation, part-wise facial characterization is combined with the recently popular sparse representation approach to address these challenges. The sparse representation approach requires several images per subject in the gallery to function effectively which is often not available in several use-cases, as in the problem we address in this work. The proposed formulation utilizes images from sequestered non-gallery subjects with similar local facial characteristics to fulfill this requirement. Extensive experiments conducted on a recently introduced plastic surgery database [17] consisting of 900 subjects highlight the effectiveness of the proposed approach.

Published in:

Applications of Computer Vision (WACV), 2012 IEEE Workshop on

Date of Conference:

9-11 Jan. 2012