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Fast and robust head detection with arbitrary pose and occlusion

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Abstract

Head detection in images and videos plays an important role in a wide range of computer vision and surveillance applications. Aiming to detect heads with arbitrarily occluded faces and head pose, in this paper, we propose a novel Gaussian energy function based algorithm for elliptical head contour detection. Starting with the localization of head and shoulder by an improved Gaussian Mixture Model (GMM) approach, the precise head contour is obtained by making use of the Omega shape formed from the head and shoulder. Experimental results on several benchmark datasets demonstrate the superiority of the proposed idea over the state-of-the-art in both detection accuracy and processing speed, even though there are various types of severe occlusions in faces.

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Acknowledgments

This research is partly supported by NSFC, China (No: 61273258, 61105001).

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Authors and Affiliations

  1. Shanghai Jiao Tong University, Institute of Image Processing and Pattern Recognition, 800 Dongchuan Road, Shanghai, China, 200000

    Tao Zhang, Zhijie Yang & Jie Yang

  2. Faculty of Engineering and Information Technology, University of Technology, Sydney, PO Box 123, Broadway, NSW, 2007, Australia

    Wenjing Jia, Qiang Wu & Xiangjian He

Authors
  1. Tao Zhang
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  2. Zhijie Yang
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  4. Qiang Wu
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  5. Jie Yang
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Correspondence to Tao Zhang.

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Zhang, T., Yang, Z., Jia, W. et al. Fast and robust head detection with arbitrary pose and occlusion. Multimed Tools Appl 74, 9365–9385 (2015). https://doi.org/10.1007/s11042-014-2110-3

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