An FPGA-based Parallel Hardware Architecture for Real-Time Face Detection Using a Face Certainty Map

This paper presents an FPGA-based parallel hardware architecture for real-time face detection. An image pyramid with twenty depth levels is generated using the input image. For these scaled-down images, a local binary pattern transform and feature evaluation are performed in parallel by using the proposed block RAM-based window processing architecture. By sharing the feature look-up tables between two corresponding scaled-down images, we can reduce the use of routing resources by half. For prototyping and evaluation purposes, the hardware architecture was integrated into a Virtex-5 FPGA. The experimental result shows around 300 frames per second speed performance for processing standard VGA (640×480×8) images. In addition, the throughput of the implementation can be adjusted in proportion to the frame rate of the camera, by synchronizing each individual module with the pixel sampling clock.