Last modified: 2019-04-24
Abstract
Since the security and identification has become a vital part from day to day` biometric security is better and more secure method in replacing old system. From capturing feature, the samples transformed using mathematical function and convert it into biometric pattern and storing the patterns in database and allow comparisons to be made between the templates. It is noticed that recognizing high complex iris patterns is difficult and sometimes the results are less accurate with other approaches. In this paper, two artificial neural network algorithms, Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) were evaluated and compared for their ability and accuracy to achieve high complex iris recognition using CASIA iris image datasets. The iris recognition for both algorithms BPNN and LVQ are performed by iris localization and iris normalization, and creation of information about iris images through iris pattern identification. Performances and accuracy in both BPNN and LVQ are compared and discussed.
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