
@conference {AveJon18A,
	title = {An FPGA-based Hardware Accelerator for Iris Segmentation},
	booktitle = {Proceedings of the International Conference on ReConFigurable Computing and FPGAs (ReConFig)},
	year = {2018},
	month = {December},
	abstract = {Biometric authentication is becoming an increasingly prevalent way to identify a person based on unique physical traits such as their fingerprints, face, and iris. The iris stands out particularly among these traits due to its relative invariability with time and high uniqueness. However, iris recognition without special, dedicated tools like near-infrared (NIR) cameras and stationary high-performance computers is a challenge. Solutions have been proposed to target mobile platforms like smart phones and tablets by making use of the Red-Green-Blue (RGB) camera commonly found on those platforms. These solutions tend to be slower than the former due to the reduced performance available in mobile processors. In this paper, we detail a Field Programmable Gate Array (FPGA)-based System on Chip (SoC) approach to help address the mobility and performance challenges that exist in current iris segmentation solutions. Our SoC architecture allows us to run the iris recognition system in software, while accelerating slower parts of the system by using parallel, dedicated hardware modules. Our approach showed a speedup in segmentation of 22x when compared to an x86-64 platform and 468x when compared to an ARMv7 platform.},
	author = {Joe Avey and Phillip Jones and Joseph Zambreno}
}
