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Overlapping Local Phase Feature (OLPF) for Robust Face Recognition in Surveillance

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7517))

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Abstract

As a non-invasive biometric method, face recognition in surveillance is a very challenging problem because of the concurrence of conditions, such as under the variable illumination with uncontrolled pose and movement in low-resolution of subject. In this paper, we present a robust human face recognition system for surveillance. Unlike traditional recognition system which detect face region directly, we use a Cascade Head-Shoulder Detector (CHSD) and a trained human body model to find the face region in an image. To recognize human face, an efficient feature, Overlapping Local Phase Feature (OLPF), is proposed, which is robust to pose and blurring without adversely affecting discrimination performance. To describe the variations of faces, Adaptive Gaussian Mixture Model (AGMM) is proposed which can describe the distributions of the face images. Since AGMM does not need the topology of face, the proposed method is resistant to the face detection errors caused by wrong or no alignment. Experimental results demonstrate the robustness of our method on public dataset as well as real data from surveillance camera.

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Author information

Authors and Affiliations

  1. Dept. of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong

    Qiang Liu & King Ngi Ngan

Authors
  1. Qiang Liu
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  2. King Ngi Ngan
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Editor information

Editors and Affiliations

  1. DGA, 7-9 rue des mathurins, 92 221, Bagneux, France

    Jacques Blanc-Talon

  2. Telecommunications and Information processing (TELIN), Ghent University, St.-Pietersnieuwstraat 41, 9000, Ghent, Belgium

    Wilfried Philips

  3. CSIRO ICT Centre, Epping, Po Box 76, 1710, Sydney, NSW, Australia

    Dan Popescu

  4. University of Antwerp, Universiteitsplein 1, Building N. 2610,, Wilrijk, Belgium

    Paul Scheunders

  5. Faculty of Information Technology, Brno University of Technology, 61266, Brno, Czech Republic

    Pavel Zemčík

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© 2012 Springer-Verlag Berlin Heidelberg

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Liu, Q., Ngan, K.N. (2012). Overlapping Local Phase Feature (OLPF) for Robust Face Recognition in Surveillance. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2012. Lecture Notes in Computer Science, vol 7517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33140-4_22

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  • DOI: https://doi.org/10.1007/978-3-642-33140-4_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33139-8

  • Online ISBN: 978-3-642-33140-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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