Fpga-based face detection system using Haar classifiers

Authors:
Junguk Cho;Shahnam Mirzaei;Jason Oberg;Ryan Kastner
Affiliations:
University of California, San Diego, La Jolla, CA, USA;University of California, Santa Barbara, Santa Barbara, CA, USA;University of California, Santa Barbara, Santa Barbara, CA, USA;University of California, San Diego, La Jolla, CA, USA
Venue:
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
Year:
2009

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Abstract

This paper presents a hardware architecture for face detection based system on AdaBoost algorithm using Haar features. We describe the hardware design techniques including image scaling, integral image generation, pipelined processing as well as classifier, and parallel processing multiple classifiers to accelerate the processing speed of the face detection system. Also we discuss the optimization of the proposed architecture which can be scalable for configurable devices with variable resources. The proposed architecture for face detection has been designed using Verilog HDL and implemented in Xilinx Virtex-5 FPGA. Its performance has been measured and compared with an equivalent software implementation. We show about 35 times increase of system performance over the equivalent software implementation.