3 research outputs found
Adaptive noise reduction and code matching for IRIS pattern recognition system
Among all biometric modalities, iris is becoming more popular due to its high performance in recognizing or verifying individuals. Iris recognition has been used in numerous fields such as authentications at prisons, airports, banks and healthcare. Although iris recognition system has high accuracy with very low false acceptance rate, the system performance can still be affected by noise. Very low intensity value of eyelash pixels or high intensity values of eyelids and light reflection pixels cause inappropriate threshold values, and therefore, degrade the accuracy of system. To reduce the effects of noise and improve the accuracy of an iris recognition system, a robust algorithm consisting of two main components is proposed. First, an Adaptive Fuzzy Switching Noise Reduction (AFSNR) filter is proposed. This filter is able to reduce the effects of noise with different densities by employing fuzzy switching between adaptive median filter and filling method. Next, an Adaptive Weighted Shifting Hamming Distance (AWSHD) is proposed which improves the performance of iris code matching stage and level of decidability of the system. As a result, the proposed AFSNR filter with its adaptive window size successfully reduces the effects ofdifferent types of noise with different densities. By applying the proposed AWSHD, the distance corresponding to a genuine user is reduced, while the distance for impostors is increased. Consequently, the genuine user is more likely to be authenticated and the impostor is more likely to be rejected. Experimental results show that the proposed algorithm with genuine acceptance rate (GAR) of 99.98% and is accurate to enhance the performance of the iris recognition system
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Realization of a universal patient identifier for electronic medical records through biometric technology
The technology exists for the migration of healthcare data from its archaic paper-based system to an electronic one, and, once in digital form, to be transported anywhere in the world in a matter of seconds. The advent of universally accessible healthcare data has benefited all participants, but one of the outstanding problems that must be addressed is how the creation of a standardized nationwide electronic healthcare record system in the United States would uniquely identify and match a composite of an individual's recorded healthcare information to an identified individual patients out of approximately 300 million people to a 1:1 match. To date, a few solutions to this problem have been proposed that are limited in their effectiveness. We propose the use of biometric technology within our fingerprint, iris, retina scan, and DNA (FIRD) framework, which is a multiphase system whose primary phase is a multilayer consisting of these four types of biometric identifiers: 1) fingerprint; 2) iris; 3) retina scan; and 4) DNA. In addition, it also consists of additional phases of integration, consolidation, and data discrepancy functions to solve the unique association of a patient to their medical data distinctively. This would allow a patient to have real-time access to all of their recorded healthcare information electronically whenever it is necessary, securely with minimal effort, greater effectiveness, and ease