REFRESH: REDEFINE for Face Recognition Using SURE Homogeneous Cores

Abstract

In this paper we present design and analysis of a scalable real-time Face Recognition (FR) module to perform 450 recognitions per second. We introduce an algorithm for FR, which is a combination of Weighted Modular Principle Component Analysis and Radial Basis Function Neural Networks. This algorithm offers better recognition accuracy in various practical conditions than algorithms used in existing architectures for real-time FR. To meet real-time requirements, a Scalable Parallel Pipelined Architecture (SPPA) is developed by realizing the above FR algorithm as independent parallel streams and sub-streams of computations. SPPA is capable of supporting large databases maintained in external (DDR) memory. By casting the computations in a stream into hardware, we present the design of a Scalable Unit for Region Evaluation (SURE) core. Using SURE cores as computer elements in a massively parallel CGRA, like REDFINE, we provide a FR system on REDEFINE called REFRESH. We report FPGA and ASIC synthesis results for SPPA and REFRESH. Through analysis using these results, we show that excellent scalability and added programmability in REFRESH makes it a flexible and favorable solution for real-time FR.