Spiking neural network and wavelets for hiding iris data in digital images

This paper introduces an efficient approach to protect the ownership by hiding the iris data into a digital image for authentication purposes. The idea is to secretly embed an iris code data into the content of the image, which identifies the owner. Algorithms based on Biologically inspired Spiking Neural Networks, called Pulse Coupled Neural Network (PCNN) are first applied to increase the contrast of the human iris image and adjust the intensity with the median filter. It is followed by the PCNN segmentation algorithm to determine the boundaries of the human iris image by locating the pupillary boundary and limbus boundary of the human iris for further processing. A texture segmentation algorithm for isolating the iris from the human eye in a more accurate and efficient manner is presented. A quad tree wavelet transform is first constructed to extract the texture feature. Then, the Fuzzy c-Means (FCM) algorithm is applied to the quad tree in the coarse-to-fine manner by locating the pupillary boundary (inner) and outer (limbus) boundary for further processing. Then, iris codes (watermark) are extracted that characterizes the underlying texture of the human iris by using wavelet theory. Then, embedding and extracting watermarking methods based on Discrete Wavelet Transform (DWT) to insert and extract the generated iris code are presented. The final process deals with the authentication process. In the authentication process, Hamming distance metric that measure the variation between the recorded iris code and the corresponding extracted one from the watermarked image (Stego image) to test weather the Stego image has been modified or not is presented. Simulation results show the effectiveness and efficiency of the proposed approach.