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 [10]. 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 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 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 frequency domain between each sub-image and the weights of the hidden layer. © Springer-Verlag 2001.
CITATION STYLE
El-Bakry, H. (2001). Fast iris detection for personal verification using modular neural nets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2206 LNCS, pp. 269–283). Springer Verlag. https://doi.org/10.1007/3-540-45493-4_31
Mendeley helps you to discover research relevant for your work.