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Fast and accurate face detection by sparse Bayesian extreme learning machine

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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.

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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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Authors and Affiliations

  1. Department of Computer and Information Science, University of Macau, Macau, SAR, China

    Chi Man Vong, Keng Iam Tai & Chi Man Pun

  2. Department of Electromechanical Engineering, University of Macau, Macau, SAR, China

    Pak Kin Wong

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  1. Chi Man Vong
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  2. Keng Iam Tai
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  3. Chi Man Pun
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  4. Pak Kin Wong
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Correspondence to Chi Man Vong.

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Vong, C.M., Tai, K.I., Pun, C.M. et al. Fast and accurate face detection by sparse Bayesian extreme learning machine. Neural Comput & Applic 26, 1149–1156 (2015). https://doi.org/10.1007/s00521-014-1803-x

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