Skip to main content


  • Research Article
  • Open Access

A Reconfigurable Architecture for Rotation Invariant Multi-View Face Detection Based on a Novel Two-Stage Boosting Method

EURASIP Journal on Advances in Signal Processing20092009:917354

  • Received: 30 December 2008
  • Accepted: 19 August 2009
  • Published:


We present a reconfigurable architecture model for rotation invariant multi-view face detection based on a novel two-stage boosting method. A tree-structured detector hierarchy is designed to organize multiple detector nodes identifying pose ranges of faces. We propose a boosting algorithm for training the detector nodes. The strong classifier in each detector node is composed of multiple novelly designed two-stage weak classifiers. With a shared output space of multicomponents vector, each detector node deals with the multidimensional binary classification problems. The design of the hardware architecture which fully exploits the spatial and temporal parallelism is introduced in detail. We also study the reconfiguration of the architecture for finding an appropriate tradeoff among the hardware implementation cost, the detection accuracy, and speed. Experiments on FPGA show that high accuracy and marvelous speed are achieved compared with previous related works. The execution time speedups range from 14.68 to 20.86 for images with size of up to when our FPGA design (98 MHz) is compared with software solution on PC (Pentium 4 2.8 GHz).


  • Information Technology
  • Quantum Information
  • Face Detection
  • Full Article
  • Rotation Invariant

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, 410073, China
Institute of Computer, School of Computer, National University of Defense Technology, Changsha, 410073, China


© Jinbo Xu et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.