Iris pattern is the most reliable biometric in terms of recognition and identification performance. Nowadays iris recognition base on video is growing rapidly. In order to obtain a robust identification system, it requires a fast and accurate iris segmentation method to improve the recognition rate. In this paper we perform four optimizations to automates our previous segmentation method and re...

High resolution images not only provide high recognition rate but also useful in safeguarding the iris recognition system from fake iris attack. To safeguard the iris recognition system against fake irises, one of the very popular technique is to detect the change in pupil size due to change in illumination. Many of existing methods assume that iris and pupil are circular or elliptical in natur...

in the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. to the best of our knowledge, the effect of noise factors on feature extraction has not been c...

Since iris muscles control the size of the pupil, iris pattern shown in a captured iris image deforms significantly during pupil dilation/constriction. Daugman-based approaches assume the iris deformation is linear when mapping the iris to the polar rectangular format. However, iris deformation is not linear and in fact differs from person to person. To cope with the non-linear iris deformation...

An overview of the iris image segmentation methodologies for biometric purposes is presented. The main focus is on the analysis of the ability of segmentation algorithms to process images with heterogeneous characteristics, simulating the dynamics of a non-cooperative environment. The accuracy of the four selected methodologies on the UBIRIS database is tested and, having concluded about their ...

The success of iris recognition depends mainly on two factors: image acquisition and an iris recognition algorithm. In this study, we present a system that considers both factors and focuses on the latter. The proposed algorithm aims to find out the most efficient wavelet family and its coefficients for encoding the iris template of the experiment samples. The algorithm implemented in software ...

The use of biometric signatures, instead of tokens such as identification cards or computer passwords, continues to gain increasing attention as an efficient means of identification and verification of individuals for controlling access to secured areas, materials, or systems and a wide variety of biometrics has been considered over the years in support of these challenges. Iris recognition is ...

Human iris recognition is one of the prominent type of biometric identification system. Iris recognition systems use texture modeling for the segmentation and classification of iris images. Texture modeling is an active research field and different techniques have been exhaustively studied for this purpose, including wavelet transform. However, due to the limited capability for capturing direct...

Iris recognition systems capture an image from an individual’s eye. The iris in the image is then segmented and normalized for feature extraction process. The performance of iris recognition systems highly depends on segmentation and normalization. For instance, even an effective feature extraction method would not be able to obtain useful information from an iris image that is not segmented or...