Multiple Face Detection and Recognition for System-an-Chip FPGAs

Recognizing multiple faces within a single frame or image presents a significant challenge in facial recognition tasks. This challenge demands robust algorithms capable of handling variations in facial position, unstable brightness, and the presence of accessories such as glasses or hats. However, in current surveillance applications, multiple face recognition is vital for maintaining various security measures. In such applications, there is a need for processors that are compact and deployable in diverse environments. FPGA is popular due to its compact size, cost-effectiveness, and high performance. This paper presents the implementation of multiple face detection and recognition on an SoC FPGA utilizing the Xilinx Kria KV260 platform. We chose the MTCNN algorithm for multiple face detection and the ResNet-20 model for multiple face recognition developed by Vitis AI, and we fine-tuned this model using the CUFace dataset. Our results demonstrate the robust capability to detect and recognize faces of up to 8 individuals within a single frame in just 0.11 seconds, with a recognition rate of 93.77%.