Skip to main content

Improving Face Detection

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7244)

Abstract

A novel Genetic Programming approach for the improvement of the performance of classifier systems through the synthesis of new training instances is presented. The approach relies on the ability of the Genetic Programming engine to identify and exploit shortcomings of classifier systems, and generate instances that are misclassified by them. The addition of these instances to the training set has the potential to improve classifier’s performance. The experimental results attained with face detection classifiers are presented and discussed. Overall they indicate the success of the approach.

Keywords

  • Face detection
  • Haar cascade

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   54.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   69.99
Price excludes VAT (Canada)
  • 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baro, X., Escalera, S., Vitria, J., Pujol, O., Radeva, P.: Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification. IEEE Transactions on Intelligent Transportation Systems 10(1), 113–126 (2009)

    CrossRef  Google Scholar 

  2. Chen, J., Chen, X., Gao, W.: Resampling for face detection by self-adaptive genetic algorithm. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 822–825 (August 2004)

    Google Scholar 

  3. Dubey, D.: Face detection using genetic algorithm and neural network. International Journal of Science and Advanced Technology 1(6), 104–109 (2011) ISSN 2221-8386

    MathSciNet  Google Scholar 

  4. Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting (1995)

    Google Scholar 

  5. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001)

    CrossRef  Google Scholar 

  6. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust Face Detection Using the Hausdorff Distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)

    CrossRef  Google Scholar 

  7. Krawiec, K., Howard, D., Zhang, M.: Overview of Object Detection and Image Analysis by Means of Genetic Programming Techniques. In: Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007, pp. 779–784 (2007)

    Google Scholar 

  8. Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003)

    CrossRef  Google Scholar 

  9. Lienhart, R., Maydt, J.: An Extended Set of Haar-Like Features for Rapid Object Detection. In: Proceedings of the 2002 International Conference on Image Processing, vol. 1, pp. 900–903 (2002)

    Google Scholar 

  10. Machado, P., Cardoso, A.: All the truth about NEvAr. Applied Intelligence, Special Issue on Creative Systems 16(2), 101–119 (2002)

    MATH  Google Scholar 

  11. Machado, P., Romero, J., Manaris, B.: Experiments in Computational Aesthetics. In: The Art of Artificial Evolution, Springer, Heidelberg (2007)

    Google Scholar 

  12. Mayer, H.A., Schwaiger, R.: Towards the evolution of training data sets for artificial neural networks. In: IEEE International Conference on Evolutionary Computation, pp. 663–666 (April 1997)

    Google Scholar 

  13. Papageorgiou, C.P., Oren, M., Poggio, T.: A general framework for object detection. In: Sixth International Conference on Computer Vision, pp. 555–562 (January 1998)

    Google Scholar 

  14. Sha, S., Jianer, C., Ling, Q., Sanding, L.: Evolutionary mechanism and implemention for recognition of objects in dynamic vision. In: 4th International Conference on Computer Science Education, ICCSE 2009, pp. 178–182 (2009)

    Google Scholar 

  15. Sims, K.: Artificial Evolution for Computer Graphics. ACM Computer Graphics 25, 319–328 (1991)

    CrossRef  Google Scholar 

  16. Spector, L., Alpern, A.: Criticism, culture, and the automatic generation of artworks. In: Proceedings of Twelfth National Conference on Artificial Intelligence, pp. 3–8. AAAI Press/MIT Press, Seattle, Washington (1994)

    Google Scholar 

  17. Teller, A., Veloso, M.: Algorithm evolution for face recognition: what makes a picture difficult. In: IEEE International Conference on Evolutionary Computation 1995 (1995)

    Google Scholar 

  18. Ventura, D., Andersen, T., Martinez, T.R.: Using evolutionary computation to generate training set data for neural networks. In: Proceedings of the International Conference on Neural Networks and Genetic Algorithms, pp. 468–471 (1995)

    Google Scholar 

  19. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, vol. 1, pp. I–511–I–518 (2001)

    Google Scholar 

  20. Yang, M.-H., Roth, D., Ahuja, N.: A snow-based face detector. In: Advances in Neural Information Processing Systems 12, pp. 855–861. MIT Press (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Machado, P., Correia, J., Romero, J. (2012). Improving Face Detection. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds) Genetic Programming. EuroGP 2012. Lecture Notes in Computer Science, vol 7244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29139-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29139-5_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29138-8

  • Online ISBN: 978-3-642-29139-5

  • eBook Packages: Computer ScienceComputer Science (R0)