Development of Iris Security System Using Adaptive Quality-Based Template Fusion

Abstract

Recently, the interface of computer technologies and biology has an enormous impact on society. Human recognition research projects promise a new life to various security consulting. Iris recognition is considered to be one of the exceedingly reliable authentication systems. To account for iris data variations, nearly of all iris systems store multiple templates per each user. Approaching to overcome the storage space and computation overheads, this paper proposes an intelligent fusion technique, algorithms, and suggestions. The quality of the input image has been checked firstly to ensure that “qualified iris samples” will be only treated. The proposed system, with the aid of the image selection stage and template quality test, has the advantage of being adaptive and simple, but can come at the expense of reject extremely inadequate data for any user. Complete the eye shape using the convex hull is the main key for image selection module. Shape-based thresholding is integrated with morphological features to avoid the dark iris problems, elliptical pupil and iris shapes. For best recognition rate, the optimum values of the 1D log Gabor filter parameters and different sizes of the iris code are recorded. From experimental results, an HD value of 0.4 can be chosen as a suitable separation point, and the optimum code size was found to be 20 × 480. An experimental work reveals a reduction in database size by nearly a 78 % and an increase of verification speed of about 85.25 % is achieved while 14.75 % of computation time in the shifting process is only required. Comparing with existing algorithms, the proposed algorithm gives an accuracy of 96.52 and 99.72 % for TRR and TAR, respectively for closed loop tested dataset. In addition, with a number of experimentations, the proposed algorithm gives the lowest FAR and FRR of 0.0019 and 0.00287 % respectively and EER of 0.0024 %.