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Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is the most reliable biometric in terms of recognition and identification performance. However, performance of these systems is affected by poor quality imaging. In this work, we extend previous research efforts on iris quality assessment by analyzing the effect of(More)
The popularity of iris biometric has grown considerably over the past two to three years. It has resulted in the development of a large number of new iris encoding and processing algorithms. Since there are no publicly available large-scale and even medium-size data bases, neither of the newly designed algorithms has undergone extensive testing. The(More)
A person only has two irises – if his pattern is stolen he quickly runs out of alternatives. Thus methods that protect the true iris pattern need to be adopted in practical biometric applications. In particular, it is desirable to have a system that can generate a new unique pattern if the one being used is lost, or generate different unique patterns for(More)
Iris biometric is one of the most reliable biometrics with respect to performance. However, this reliability is a function of the ideality of the data. One of the most important steps in processing nonideal data is reliable and precise segmentation of the iris pattern from remaining background. In this paper, a segmentation methodology that aims at(More)
In the field of iris-based recognition, evaluation of quality of images has a number of important applications. These include image acquisition, enhancement, and data fusion. Iris image quality metrics designed for these applications are used as figures of merit to quantify degradations or improvements in iris images due to various image processing(More)
Iris segmentation is an important first step for high accuracy iris recognition. A robust iris segmentation procedure should be able to handle noise, occlusion and non-uniform lighting. It also impacts system accuracy - high FAR or FRR values may come directly from bad or wrong segmentations. In this paper a simple new approach for iris segmentation is(More)
Recent developments in the field of nonideal iris recognition have shown that the presence of the degradations such as insufficient contrast, unbalanced illumination, out-of-focus, motion blur, specular reflections, and partial area affect performance of iris recognition systems. Most iris recognition systems are designed to implement a number of processing(More)
An adaptive method to predict NIR channel image from color iris images is introduced. Both visual inspection of the predicted image and the verification performance indicate that the adaptive mapping linking NIR image and color image is a potential solution to the problem of matching NIR images vs. color images in practice. When matched against NIR enrolled(More)