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Neural Computing and Applications

, Volume 26, Issue 5, pp 1149–1156 | Cite as

Fast and accurate face detection by sparse Bayesian extreme learning machine

  • Chi Man VongEmail author
  • Keng Iam Tai
  • Chi Man Pun
  • Pak Kin Wong
Original Article

Abstract

Real-time face detection is an important research topic in computer vision and pattern recognition. One of the effective methods in face detection is model-based approach which employs neural network technique for the construction of classification model. Relevant techniques such as support vector machines are fast in training an accurate model which is, however, relatively slow in execution time. The reason is due to the large size of the constructed model. In this paper, the main contribution is to apply a new method called sparse Bayesian extreme learning machine (SBELM) for real-time face detection because SBELM can minimize the model size with nearly no compromise on the accuracy and have fast execution time. Several benchmark face datasets were employed for the evaluation of SBELM against other state-of-the-art techniques. Experimental results show that SBELM achieves fastest execution time with high accuracy over the benchmark face datasets. A MATLAB toolbox of SBELM is also available on our Web site.

Keywords

Face recognition Face detection Sparse Bayesian Extreme learning machine 

Notes

Acknowledgments

The work is supported by Fundo para o Desenvolvimento das Ciencias e da Tecnologia (Grant No. FDCT/075/2013/A) and University of Macau (Grant No. MYRG075(Y2-L2)-FST13-VCM).

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Copyright information

© The Natural Computing Applications Forum 2014

Authors and Affiliations

  • Chi Man Vong
    • 1
    Email author
  • Keng Iam Tai
    • 1
  • Chi Man Pun
    • 1
  • Pak Kin Wong
    • 2
  1. 1.Department of Computer and Information ScienceUniversity of MacauMacauSAR, China
  2. 2.Department of Electromechanical EngineeringUniversity of MacauMacauSAR, China

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