Furrow And Crypt Detection Using Modified Ant Colony Optimization For Iris Recognition

Zaheera, Zainal Abidin (2016) Furrow And Crypt Detection Using Modified Ant Colony Optimization For Iris Recognition. PhD thesis, Universiti Teknikal Malaysia Melaka.

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Iris recognition has been widely recognized as one of the most performing biometric system. The accuracy performance of iris recognition system is measured by FAR (False Accept Rate) and FRR (False Reject Rate). FRR measures the genuine that is incorrectly denied by the system due to the changes in iris features (such as aging and health condition) and external factors that affected the iris image to be high in noise rate. The external factors such as technical fault, occlusion, and source of lighting caused the image acquisition which produce distorted iris images problem hence incorrectly rejected by the system. The current way of reducing FRR are wavelets and Gabor filters, cascaded classifiers, ordinal measure, multiple biometric modality and selection of unique iris features. Iris structure consists of unique features such as crypts, furrows, collarette, pigment blotches, freckles and pupil that are distinguishable among human. Previous research has been done in selecting the unique iris features however it shows low accuracy performance. As a solution, to improve the accuracy performance, this research proposes a new approach called as Modified Ant Colony Optimization that uses ant colony algorithm which search for crypts and radial furrow. The method consists of two tasks in obtaining the crypt and radial furrow features from the iris texture. The first task is the artificial ants that scan the pixel values according to the range of selected crypt or radial furrow. Then, the scanned pixels value is searched based on degree of angle (0o, 45o, 90o and 135o). The second task produces the confusion matrix and the blob of iris feature image is marked and indexed before stored into the database. In order to evaluate the performance of the proposed approach, FAR and FRR are measured with Chinese Academy of Sciences' Institute of Automation (CASIA) database for high quality images and Noisy Visible Wavelength Iris Image Databases (UBIRIS) database for noisy iris. By using CASIA version 3 image databases, the crypt feature shows that the result of FRR is 18.05% and radial furrow gives 81.5% when FAR at 0.1%. For UBIRIS version 1 database, the crypt feature indicates that the value FRR is 46.93% meanwhile the radial furrow shows the values of FRR 33.87% when FAR at 0.1%. To evaluate Modified Ant Colony Optimization, the genuine acceptance value (GAR) is measured to recognize iris features detection in low quality image environment. The experiment finding indicates that by using the Modified Ant Colony Optimization, radial furrow is able to be detected in distorted iris images with 84.62% since its own characteristics is obviously revealed. Moreover, the intersaction between FAR and FRR produces the Equal Error Rate (EER) with 0.21%, which indicated that equal error rate is lower than the previous standard value, which is 0.3%. Therefore, the advantages of using Modified Ant Colony Optimization are it has the capability to adapt with unique iris features in robust manner and use small amount of information in unique micro-characteristics of iris features to determine the user. The outcome of this new approach is to reduce the EER rates since lower EER rates indicates better accuracy performance. As a conclusion, the contribution of Modified Ant Colony Optimization extraction approach brings an innovation at the extraction process in the biometric technology and provides benefits to the communities.

Item Type: Thesis (PhD)
Uncontrolled Keywords: Identification -- Automation, Pattern recognition system, Iris (Eye), Computer security, Biometric identification
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Library > Disertasi > FTMK
Depositing User: Ahmad Tarmizi Abdul Hadi
Date Deposited: 09 Aug 2016 04:00
Last Modified: 09 Aug 2016 04:00
URI: http://eprints.utem.edu.my/id/eprint/16924
Statistic Details: View Download Statistic

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