Iris recognition in unconstrained environment on graphic processing units with CUDA

摘要

Newly introduced Iris recognition systems (IRSs) run on serial processors. In this paper, an alternative method has been introduced for parallel processing on Graphic processing unit (GPU) with Compute unified device architecture (CUDA) in order to increase the speed of the system. The IRS has two main parallel processing criteria, which include the division of computations into hundreds of independent units and the time of calculation more than the time of transferring from the GPU. The IRS is divided into six stages including imaging, pre-processing, segmentation, normalization, feature extraction, and matching. In order to increase speed and accuracy, two stages of iris segmentation and matching play an important role in the IRS. In this paper parallel execution of an identical algorithm for these two stages has been used. The reason for paralleling the iris segmentation stage and their low speed matching is due to a great amount of information in the iris database, plenty of calculations and lack of data dependency in these two stages. For parallelism at the segmentation stage, for each radius, the Hough transform (HT) is a processor, and in the matching stage two parts are considered: The first part consists of 32 actions comparing the input code with the database code in parallel and in the second part 2048 bits with the use of threads on each processor is performed in two sub-sections in pairs of bits and in parallel with each other. Finally, the two-way coding is achieved. In compare of existing methods, this method has rather more accurate and is also superior in terms of processing time on the GPU with CUDA. The results of the implementation of the above method on the images in UBIRIS, BATH, CASIA and MMUI databases show that the proposed method has a precision accuracy of 99.12%, 97.98%, 98.80% and 98.34%, respectively, and the average speedup for parallel processing of images in the database in the proposed method on the GPU with CUDA are 18.8, 14.7, 18, and 19 times, respectively.