DocumentCode :
3015174
Title :
Graphical Model Approach to Iris Matching Under Deformation and Occlusion
Author :
Kerekes, R. ; Narayanaswamy, B. ; Thornton, J. ; Savvides, M. ; Kumar, B. V K Vijaya
Author_Institution :
Carnegie Mellon Univ., Pittsburgh
fYear :
2007
fDate :
17-22 June 2007
Firstpage :
1
Lastpage :
6
Abstract :
Template matching of iris images for biometric recognition typically suffers from both local deformations between the template and query images and large occlusions from the eyelid. In this work, we model deformation and occlusion as a set of hidden variables for each iris comparison. We use afield of directional vectors to represent deformation and a field of binary variables to represent occlusion. We impose a probability distribution on these fields using a lattice-type undirected graphical model, in which the graph edges represent interdependencies between neighboring iris regions. Gabor wavelet-based similarity scores and intensity statistics are used as observations in the model. Loopy belief propagation is applied to estimate the conditional distributions on the hidden variables, which are in turn used to compute final match scores. We present underlying theory as well as experimental results from both the CASIA iris database and the database provided for the iris challenge evaluation (ICE). We show that our proposed method significantly improves recognition accuracy on these datasets over existing methods.
Keywords :
Gabor filters; graph theory; image matching; image representation; probability; queueing theory; wavelet transforms; Gabor wavelet-based similarity; binary variables; biometric recognition; directional vectors; graphical model approach; intensity statistics; iris matching; lattice-type undirected graphical model; local deformations; loopy belief propagation; occlusion representation; probability distribution; query images; template matching; Belief propagation; Biometrics; Databases; Deformable models; Eyelids; Graphical models; Image recognition; Iris; Probability distribution; Statistical distributions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on
Conference_Location :
Minneapolis, MN
ISSN :
1063-6919
Print_ISBN :
1-4244-1179-3
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2007.383109
Filename :
4270134
Link To Document :
بازگشت