A novel method for iris feature extraction based on intersecting cortical model network

Iris recognition has received increasing attentions due to its distinct characteristics in recent years. An efficient approach for iris feature extraction plays a very important role in an iris recognition system. In this paper, we developed a novel method for iris feature extraction utilizing the Intersecting Cortical Model (ICM) network which is a simplified model of Pulse-Coupled Neural Network (PCNN) model. In our research, the normalized iris image was imported into an ICM network after enhancement processing. Then the third output pulse image produced by the ICM network was chosen as the iris code. In order to estimate the performance of our iris feature extraction method, an iris recognition platform is produced and the Hamming Distance between two iris codes is computed to measure the dissimilarity of them. The algorithm was tested on CASIA v1.0 iris image database and the results show that the ICM network has promising potential to extract iris textual features.