Skip to main content

FaceL: Facile Face Labeling

  • Conference paper
Computer Vision Systems (ICVS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5815))

Included in the following conference series:

Abstract

FaceL is a simple and fun face recognition system that labels faces in live video from an iSight camera or webcam. FaceL presents a window with a few controls and annotations displayed over the live video feed. The annotations indicate detected faces, positions of eyes, and after training, the names of enrolled people. Enrollment is video based, capturing many images per person. FaceL does a good job of distinguishing between a small set of people in fairly uncontrolled settings and incorporates a novel incremental training capability. The system is very responsive, running at over 10 frames per second on modern hardware. FaceL is open source and can be downloaded from http://pyvision.sourceforge.net/facel .

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bolme, D.S., Beveridge, J.R., Teixeira, M.L., Draper, B.A.: The CSU face identification evaluation system: Its purpose, features, and structure. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds.) ICVS 2003. LNCS, vol. 2626, pp. 304–313. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  2. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)

    Article  Google Scholar 

  3. Bolme, D.S., Draper, B.A., Beveridge, J.R.: Average of synthetic exact filters. In: CVPR (2009)

    Google Scholar 

  4. Chang, C.C., Lin, C.J.: LIBSVM: a Library for Support Vector Machines (2007)

    Google Scholar 

  5. Bolme, D.S.: Pyvision - computer vision toolbox. WWW Page (2008), http://pyvision.sourceforge.net

  6. Phillips, P., Scruggs, W., O’Toole, A., Flynn, P., Bowyer, K., Schott, C., Sharpe, M.: FRVT 2006 and ICE 2006 large-scale results. National Institute of Standards and Technology, NISTIR (2007)

    Google Scholar 

  7. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: CVPR (1991)

    Google Scholar 

  8. Fisher, W.: An introduction to and analysis of Scarecrow. Master’s thesis, Colorado State Univ. (2008)

    Google Scholar 

  9. Saraf, J.: An assessment of alternative features for a semi-naive Bayesian face detector on single face images. Master’s thesis, Colorado State University (2007)

    Google Scholar 

  10. Garage, W.: Opencv libarary (April 2009), http://opencv.willowgarage.com

  11. Suga, A.: Malic - malib with csufaceideval and opencv (January 2006), http://malic.sourceforge.net

  12. Pittsburgh Pattern Recognition: Webcam face tracker (April 2009), http://demo.pittpatt.com/

  13. NeuroTechnology: Verilook demo (April 2009), http://www.neurotechnology.com/download.html

  14. Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: ICIP (2002)

    Google Scholar 

  15. Beveridge, J., Alvarez, A., Saraf, J., Fisher, W., Flynn, P., Gentile, J.: Face Detection Algorithm and Feature Performance on FRGC 2.0 Imagery. In: Biometrics: Theory, Applications, and Systems (2007)

    Google Scholar 

  16. Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. LibSVM (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bolme, D.S., Beveridge, J.R., Draper, B.A. (2009). FaceL: Facile Face Labeling. In: Fritz, M., Schiele, B., Piater, J.H. (eds) Computer Vision Systems. ICVS 2009. Lecture Notes in Computer Science, vol 5815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04667-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04667-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04666-7

  • Online ISBN: 978-3-642-04667-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics