книга Swarm Intelligence for Iris Recognition

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Swarm Intelligence for Iris Recognition

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Название: Swarm Intelligence for Iris Recognition
Автор: Zaheera Zainal Abidin
Страниц: 146
Формат: PDF
Размер: 10.1 MB
Качество: Отличное
Язык: Английский
Год издания: 2022


Swarm Intelligence for Iris Recognition has been written to promote the use of Artificial Intelligence (AI) and swarm optimization methods in iris recognition. The artificial intelligence and swarm optimization method are important since they serve as great tools for learning, teaching, and research. The Swarm Intelligence (SI) inspires iris recognition to overcome the problem of false acceptance rate and false reject rate at the matching process. Swarm intelligence, artificial neural networks, and evolutionary algorithms have been the method of natural computing for making iris recognition in a better accuracy performance.



Iris recognition is one of the highest accuracy techniques used in biometric systems. The accuracy of the iris recognition system is measured by False Reject Rate (FRR), which measures the authenticity of a user who is incorrectly rejected by the system due to changes in iris features (such as aging and health condition) and external factors that affect iris image, for instance, high noise rate. External factors such as technical fault, occlusion, and source of lighting that causes the image acquisition to produce distorted iris images create error, hence are incorrectly rejected by the biometric system. FRR can be reduced using wavelets and Gabor filters, cascaded classifiers, ordinal measures, multiple biometric modalities, and a selection of unique iris features. Nonetheless, in the long duration of the matching process, existing methods were unable to identify the authenticity of the user since the iris structure itself produces a template changed due to aging. In fact, the iris consists of unique features such as crypts, furrows, collarette, pigment blotches, freckles, and pupils that are distinguishable among humans. Earlier research was done by selecting unique iris features. However, these had low accuracy levels.



A new way of identifying and matching the iris template using the nature-inspired algorithm is described in this book. It provides an overview of iris recognition that is based on nature-inspired environment technology. The book is useful for students from universities, polytechnics, community colleges; practitioners; and industry practitioners.


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