Swarm Intelligence for Iris Recognition

Iris recognition has been widely recognized as one of the most performing biometric system. The accuracy performance of iris recognition system is measured by FRR (False Reject Rate). FRR measures the genuine user who is incorrectly denied by the systemdue 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 to produce distorted iris images problem hence incorrectly rejected by the biometric 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. Nonetheless, in the long duration of matching process, the previous methods unable to identify the user as a genuine since the iris structure itself produce a template changed due to aging. In facts, iris 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. Therefore, a new way of identifying and matching the iris template using nature-inspired algorithm is proposed in this book. As a conclusion, this book entitled as Swarm Intelligence for Iris Recognition" brings an overview of iris recognition that naturally based on nature-inspired environment technology and provides benefits to the reader"--