Furrow and crypt detection using Modified Ant Colony Optimization for iris recognition / Zaheera Zainal Abidin

• Main Author: Zainal Abidin, Zaheera
• Format: Book Section
• Language: English
• Published: Institute of Graduate Studies, UiTM 2016
• Subjects: Malaysia
• Online Access: http://ir.uitm.edu.my/id/eprint/19387/1/ABS_ZAHEERA%20ZAINAL%20ABIDIN%20TDRA%20VOL%209%20IGS%2016.pdf; http://ir.uitm.edu.my/id/eprint/19387/

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…