A segmentation method to improve iris-based person identification

The authentication of individuals using iris-based recognition is a widely developing technology. Precise and unobtrusive image acquisition is not always possible. This introduces a number of ill-affecting factors to the subsequent localisation and characterisation stages. Common problems include variations in lighting, poor image quality, noise and interference caused by eyelashes. The feature extraction and classification procedures rely heavily on the rich textural details of the iris to provide a unique digital signature for an individual. As a result, the stability and integrity of a system depends on effective segmentation of the iris to generate the iris-code. The previously mentioned problems hinder this task. A new segmentation method is presented to tackle these problems. Principal component analysis (PCA) is discussed as a preprocessing technique that removes redundant and useless data. Variations in lighting; and noise are handled using an application of median filtering and adaptive thresholding. Finally, edge detection and the Hough transform are discussed for locating the circular boundaries of the pupil and sclera