Approximate Computing for Iris Recognition Systems

Abstract

Leveraging the error tolerance characteristics of many emerging applications, approximate computing techniques aim to trade-off small amount of inaccuracies in the computation to significantly reduce computational resources such as runtime, power, and design area. Approximate computing has been successfully applied to a wide range of areas including computer vision and machine learning. In this chapter, we demonstrate a novel application of approximate computing techniques in the field of biometric security by providing a comprehensive iris recognition system case study. Our system consists of an end-to-end flow, which captures input images of eyes from a near-infrared (NIR) camera and produces the iris encoding. The goal is to produce sufficiently accurate final encoding despite relying on intermediate approximate computational steps. Unlike previous efforts in approximate computing which typically target individual algorithms, this chapter explores a complex software/hardware pipeline system for iris code computation from live camera feed using an FPGA-based SoC. Our flow consists of four major algorithms, through which eight approximation knobs are identified for accuracy versus runtime trade-off at both the algorithmic and hardware levels. In order to explore this large design space for optimal parameter configurations, we employ reinforcement learning technique with a recurrent neural network as the learning agent. Using the proposed techniques, we demonstrate significant runtime saving of 48×, while conforming with industry-standard accuracy requirements for iris biometric systems.