Graphical Model Approach to Iris Matching Under Deformation and Occlusion
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.