Iris recognition has been developing for over 20 years, but, only in recent years has it been more accessible and widely accepted as one of the most accurate and un-obtrusive biometric modalities. Over the past few years, many companies have developed iris acquisition systems that are more user-friendly. Iris-On-the-Move (IOM) is one such system, offering significant stand-off acquisition dista...

Iris recognition is one of the emerging areas as the demand for security in social and personal areas is increasing day by day. The main and most challenging step in the process of iris recognition is iris localization. As accuracy in localization of iris significantly affect the further processing of feature extraction and template matching stages. Traditional algorithms work efficiently and a...

A biometric system uniquely identifies and authenticates humans based on their physical or behavioural features. Iris recognition is one of the most reliable methods of biometric authentication that recognizes a person by pattern of the iris. No two irises are alike -not between identical twins, or even between the left eye and the right eye of the same person. The existing works on iris segmen...

A secure identification system based on human iris recognition has been an attractive goal for researchers for a long time. In this paper, we propose an efficient iris recognition scheme. To significantly reduce the computational time that is required for locating the iris boundaries in a captured eye image, only the 3and 9o’clock sides of the iris are considered in our scheme as a search area ...

This paper presents a new user dependent approach in iris recognition systems. In the proposed method, consistent bits of iris code are calculated, based on the user specifications, using the user’s mask. Another contribution of our work is in the iris segmentation phase, where a new pointwise level set approach with area preserving has been used for determining inner and outer iris boundaries,...

Images of human iris contain specular highlights due to the reflective properties of the cornea. This corneal reflection causes many errors not only in iris and pupil center estimation but also to locate iris and pupil boundaries especially for methods that use active contour. Each iris recognition system has four steps: Segmentation, Normalization, Encoding and Matching. In order to address th...

This special issue regards the recognition of degraded iris images acquired in visible wavelengths. During 2009 and 2010, the University of Beira Interior (Portugal) promoted two International evaluation initiatives about this subject, named Noisy Iris Challenge Evaluation (NICE) I and II. The first one focussed on the evaluation of iris segmentation strategies, considering that iris data acqui...

With the aim to improve the performance of iris segmentation method to process images with heterogeneous characteristics, the authors introduce a new method inspired by the support vector domain description (SVDD). A local geometric moment function is used to extract shape features of the iris borders. Then, these features are fed into the trained SVDD classifier for borders recognition followe...

Iris recognition is a relatively new and widely developing technology. The unique and distinct spatial patterns of the iris is used to create a digital signature for person identification. A common problem faced by systems is that of accurate segmentation of the region of interest (ROI). This paper discusses various texture analysis and pattern classification techniques for characterizing the ROI.