An efficient and flexible FPGA implementation of a face detection system

Abstract

This paper proposes a hardware architecture based on the object detection system of Viola and Jones using Haar-like features. The proposed design is able to discover faces in real-time with high accuracy. Speed-up is achieved by exploiting the parallelism in the design, where multiple classifier cores can be added. To maintain a flexible design, classifier cores can be assigned to different images. Moreover using different training data, every core is able to detect a different object type. As development platform, the Zynq-7000 SoC from Xilinx is used, which features an ARM Cortex-A9 dual-core CPU and a programmable logic (FPGA). The current implementation focuses on the face detection and achieves a real-time detection at the rate of 16.53 FPS on image resolution of 640×480 pixels, which represents a speed-up of 6.46 times compared to the equivalent OpenCV software solution.