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Integrating face recognition into security systems

  • Systems and Applications
  • Conference paper
  • First Online:
Audio- and Video-based Biometric Person Authentication (AVBPA 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1206))

Abstract

Automated processing of facial images has become a serious market for both hard- and software products. For the commercial success of face recognition systems it is most crucial that the process of face image capturing is very convenient for the people exposed to such systems. As a consequence, the whole imaging setup has to be carefully designed for each operational scenario.

The paper mainly deals with the problem of face image capturing in real security applications. In this context, we use the term face recognition in a broader sense, including the important functionality of face spotting followed by face validation. The latter provides the front-end of video-based automated person authentication.

We describe two examples for a successful integration of face recognition in a security system. One is a system for recognizing authorization to use a vehicle and the other one is an automatic “assistant” for a security desk officer.

Both applications require techniques for face detection and face segmentation. Key problems are the camera setup and the size of the surveillance area. We propose an approach using a tracking camera with a high speed pan-tilt unit.

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References

  1. R. Brunelli and T. Poggio, Face Recognition: Features versus Templates, in IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 15, No. 10, pp. 1042–1052, 1993.

    Google Scholar 

  2. T. C. Chang, T. S. Huang, and C. Novak, Facial Feature Extraction from Color Images, in Proc. International Conference on Pattern Recognition, Vol. II, pp. 39–43, 1994.

    Google Scholar 

  3. Y. Dai and Y. Nakano, Face-Texture Model Based on SGLD and its Application in Face Detection in a color scene, in Pattern Recognition, Vol. 29, No. 6, pp. 1007–1017, 1996.

    Google Scholar 

  4. T. Darrel, B. Moghaddam, and A. Pentland, Active Face Tracking and Pose Estimation in an Interactive Room, in Proc. Computer Vision and Pattern Recognition, pp. 67–72, 1996.

    Google Scholar 

  5. W. Gillner, S. Bohrer, and V. Vetter, Objektverfolgung mit pyramidenbasierten optischen Flussfeldern, in Bildverarbeitung '93: Forschen, Entwickeln, Anwenden, Technische Akademie Esslingen, 1993, in German.

    Google Scholar 

  6. V. Govindaraju, D. B. Sher, R. K. Srihari, and S. N. Srihari, Locating Human Faces in Newspaper Photographs, in Proc. Computer Vision and Pattern Recognition, pp. 549–554, 1989.

    Google Scholar 

  7. A. Jacquin and A. Eleftheriadis, Automatic Location Tracking of Faces and Facial Features in Video Sequences, in Proc. International Workshop on Automatic Face-and Gesture-Recognition, pp. 142–147, 1995.

    Google Scholar 

  8. C. L. Phillips and H. T. Nagle, Digital Control System Analysis and Design, Prentice Hall, 2nd ed. 1989.

    Google Scholar 

  9. C. Ponticos, A Robust Real Time Face Location Algorithm for Videophones, in Proc. British Machine Vision Conference, pp. 449–458, 1993.

    Google Scholar 

  10. H. A. Rowley, S. Baluja, and T. Kanade, Neural Network-Based Face Detection, in Proc. Computer Vision and Pattern Recognition, pp. 203–208, 1996.

    Google Scholar 

  11. W. von Seelen et al., A Neural Architecture for Autonomous Visually Guided Robots — Results of the NAMOS Project-, Fortschr.-Ber. VDI Reihe 10 Nr. 388. VDI Verlag, 1995.

    Google Scholar 

  12. V. Vetter, G.-J. Giefing, R. Mai, and H. Weisser, Driver Face Recognition as a Security and Safety Feature, in Law Enforcement Technologies: Identification Technologies and Traffic Safety, Proc. SPIE 2511, pp. 182–190, 1995.

    Google Scholar 

  13. G. Yang and T. S. Huang, Human Face Detection in a Complex Background, in Pattern Recognition, Vol. 27, No. 1, pp. 53–63, 1994.

    Google Scholar 

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Author information

Authors and Affiliations

  1. C-VIS Computer Vision und Automation GmbH, D-44799, Bochum, Germany

    Volker Vetter & Thomas Zielke

  2. Institut für Neuroinformatik, Ruhr-Universität, D-44780, Bochum, Germany

    Werner von Seelen

Authors
  1. Volker Vetter
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  2. Thomas Zielke
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  3. Werner von Seelen
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Editor information

Josef Bigün Gérard Chollet Gunilla Borgefors

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© 1997 Springer-Verlag Berlin Heidelberg

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Vetter, V., Zielke, T., von Seelen, W. (1997). Integrating face recognition into security systems. In: Bigün, J., Chollet, G., Borgefors, G. (eds) Audio- and Video-based Biometric Person Authentication. AVBPA 1997. Lecture Notes in Computer Science, vol 1206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0016025

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  • DOI: https://doi.org/10.1007/BFb0016025

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62660-2

  • Online ISBN: 978-3-540-68425-1

  • eBook Packages: Springer Book Archive

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