An Implementation of PCA and ANN-based Face Recognition System on Coarse-grained Reconfigurable Computing Platform

Abstract

In this paper, a PCA and ANN-based face recognition system is proposed and implemented on a Coarse Grain Reconfigurable Computing (CGRC) platform. Our work is quite distinguished from previous ones in two aspects. First, a new hardware-software co-design method is proposed, and the whole face recognition system is divided into several parallel tasks implemented on both the Coarse-Grained Reconfigurable Architecture (CGRA) and the General-Purpose Processor (GPP). Second, we analyzed the source code of the ANN algorithm and proposed the optimization solution to explore its multi-level parallelism to improve the performance of the application on the CGRC platform. The computation tasks of ANN are dynamically mapped onto CGRA only when needed, and it's quite different from traditional Field Programmable Gate Array (FPGA) methods in which all the tasks are implemented statically. Implementation results show that our system works correctly in face recognition with a correct recognition rate of approximately 90.5%. To the best of our knowledge, this work is the first implementation of PCA and ANN-based face recognition system on a dynamically CGRC platform presented in the literature.