Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Amir Saleh; Yusuf Roni Laia; Fransiskus Gowasa; Victor Daniel Sihombing

doi:10.29207/resti.v7i1.4441

Amir Saleh Universitas Prima Indonesia
Yusuf Roni Laia Universitas Prima Indonesia
Fransiskus Gowasa Universitas Prima Indonesia
Victor Daniel Sihombing Universitas Prima Indonesia

DOI: https://doi.org/10.29207/resti.v7i1.4441

Keywords: Iris recognition, SOM, Hybrid SOM, Cosine similarity, Daugman's algorithm

Abstract

One of the neural network algorithms that can be used in iris recognition is self-organizing map (SOM). This algorithm has a weakness in determining the initial weight of the network, which is generally carried out randomly, which can result in a decrease in accuracy when an incorrect determination is made. The solution that is often used is to apply a hybrid process in determining the initial weight of the SOM network. This study takes an approach using the cosine similarity equation to determine the initial weight of the network SOM in order to increase recognition accuracy. In addition, the localization process needs to be carried out to limit the area of the iris image being studied so that it is easy for the recognition process to be carried out. The method proposed in this study for iris recognition, namely hybrid SOM and Daugman’s algorithm, has been tested on several people by capturing the iris of the eye using a digital camera. The captured eyes have been localized first using the Daugman’s algorithm, and then the image features were extracted using the GLCM and LBP methods. In the final stage of the study, an iris recognition comparison test was performed, and the results obtained an accuracy of 85.50% using the proposed method and an accuracy of 73.50% without performing a hybrid process on the SOM network.

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