Human iris detection using fast cooperative modular neural nets
Hazem Mokhtar El-Bakry · 2002
A combination of fast and cooperative modular neural nets to enhance the performance of the detection process is introduced. I have applied such an approach successfully to detect human faces in cluttered scenes (El-Bakry et al.) (2000). Here, this technique is used to identify human irises automatically in a given image. In the detection phase, neural nets are used to test whether a window of 20/spl times/20 pixels contains an iris or not. The major difficulty in the learning process comes from the large database required for iris/non-iris images. A simple design for cooperative modular neural nets is presented to solve this problem by dividing these data into three groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. Simulation results for the proposed algorithm show a good performance. Furthermore, faster iris detection is obtained through image decomposition into many sub-images and applying cross correlation in the frequency domain between each sub-image and the weights of the hidden layer.