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The popularity of iris biometric has grown considerably over the past 2-3 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 databases, neither of the newly designed algorithms has undergone extensive testing. The designers claim(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)
— 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(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)
Three methods to improve the performance of biometric matchers based on vectors of quality measures associated with biometric samples are described. The first two methods select samples and matching scores based on predicted values of Quality of Sample (QS) index (defined here as d-prime) and Confidence in matching Scores (CS), respectively. The third(More)
In the absence of real data for extensive testing of newly designed large-scale biometrics recognition systems a number of solutions are possible including use of resampling methods , generation of synthetic data having properties similar to real data of interest, or use of analytical tools to predict the performance. Each of the methods has its own(More)