A Real-Time Face Recognition System by Efficient Hardware-Software Co-Design on FPGA SoCs

Abstract

With the development of deep learning, the accuracy of face recognition has been significantly improved. Current face recognition systems are mostly designed for CPU or GPU platforms, and faces significant latency and power constraints when migrated to embedded devices. In this live demonstration, a real-time face recognition system based on FPGA System-on-Chip (SoC) platforms is presented. To achieve real-time processing, the face recognition algorithm based on convolutional neural network is optimized first to a hardware-friendly network model and is accelerated on FPGA, while the face detection and face alignment are implemented on ARM. The latency of the entire system is 52 ms, and the face recognition accuracy on the LWF data set reaches 99.05%.