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Human iris detection using fast cooperative modular neural nets and image decomposition

El-Bakry H. M.

Machine Graphics and Vision

2002

Vol. 11, No. 4

499-512

In this paper, a combination of fast and cooperative modular neural nets to enhance the performance of the detection process is introduced. I have applied such approach successfully to detect human faces in cluttered scenes, [11]. Here, this technique is used to identify human irises automatically in a given image. Neural nets are used to test whether a window of 20x20 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 presemted 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. Moreover, a powerful system for personal identification using iris detection is presented. Futhermore, 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.