LIGHTING-VARIABLE ADABOOST BASED-ON SYSTEM FOR ROBUST FACE DETECTION

R. Wood, J. I. Olszewska

2012

Abstract

In order to detect faces in pictures presenting difficult real-world conditions such as dark background or backlighting, we propose a new method which is robust to varying illuminations and which automatically adapts itself to these lighting changes. The proposed face detection technique is based on an efficient AdaBoost super-classifier and relies on multiple features, namely, the global intensity average value and the local intensity variations. Based on tests carried out on standards datasets, our system successfully performs in indoor as well as outdoor situations with different lighting levels.

References

  1. Ahonen, T., Hadid, A., and Pietikainen, M. (2006). Face descritpion with local binary patterns: application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12):2037-2041.
  2. Beveridge, J. R., Bolme, D. S., Draper, B. A., Givens, G. H., Liu, Y. M., and Phillips, P. J. (2010). Quantifying how lighting and focus affect face recognition performance. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pages 74-81.
  3. Crowley, J. L. (1997). Vision for man machine interaction. Robotics and Autonomous Systems, 19(3-4):347-359.
  4. Fei-Fei, L., Andreetto, M., and Ranzato, M. A. (2003). The Caltech - 101 Object Categories dataset. Available online: http://www.vision.caltech.edu/feifeili/Datasets.htm.
  5. Gundimada, S., Tao, L., and Asari, V. (2004). Face detection technique based on intensity and skin color distribution. In IEEE International Conference in Image Processing, volume 2, pages 1413-1416.
  6. Guo, B., Lam, K.-M., Lin, K.-H., and Siu, W.-C. (2003). Human face recognition based on spatially weighted Hausdorff distance. Pattern Recognition Letters, 24(1- 3):499-507.
  7. Heisele, B., Serre, T., and Poggio, T. (2007). A componentbased framework for face detection and identification. International Journal of Computer Vision, 74(2):167- 181.
  8. Huang, D.-Y., Lin, C.-J., and Hu, W.-C. (2011). Learningbased face detection by adaptive switching of skin color models and AdaBoost under varying illumination. Journal of Information Hiding and Multimedia Signal Processing, 2(3):2073-4212.
  9. Hurley, D. J., Harbab-Zavar, B., and Nixon, M. S. (2008). Handbook of Biometrics, chapter The ear as a biometric, pages 131-150. Springer-Verlag.
  10. Julian, P., Dehais, C., Lauze, F., Charvillat, V., Bartoli, A., and Choukroun, A. (2010). Automatic hair detection in the wild. In IEEE International Conference on Pattern Recognition, pages 4617-4620.
  11. Kawato, S. and Ohya, J. (2000). Real-time detection of nodding and head-shaking by directly detecting and tracking the ”Between-Eye”. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 40-45.
  12. Li, Y., Lai, J. H., and Yuen, P. C. (2006). Multi-template ASM method for feature points detection of facial image with diverse expressions. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 435-440.
  13. Lin, K., Huang, J., Chen, J., and Zhou, C. (2008). Real-time eye detection in video streams. In IEEE International Conference on Natural Computation, volume 6, pages 193-197.
  14. Olszewska, J. I., DeVleeschouwer, C., and Macq, B. (2008). Multi-feature vector flow for active contour tracking. In IEEE International Conference on Acoustics, Speech and Signal Processing, pages 721-724.
  15. Rowley, H. A., Baluja, S., and Kanade, T. (1998). Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1):23- 38.
  16. Sun, H.-M. (2010). Skin detection for single images using dynamic skin color modeling. Pattern Recognition, 43(4):1413-1420.
  17. Viola, P. and Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2):137-154.
  18. Woodward, D. L., Pundlik, S. J., Lyle, J. R., and Miller, P. E. (2010). Periocular region appearance cues for biometric identification. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pages 162-169.
  19. Yokoyama, T., Yagi, Y., and Yachida, M. (1998). Active contour model for extracting human faces. In IEEE International Conference on Pattern Recognition, volume 1, pages 673-676.
  20. Zhao, W., Chellappa, R., Phillips, P. J., and Rosenfeld, A. (2003). Face recogntion: A literature survey. ACM Computing Surveys, 35(4):399-458.
Download


Paper Citation


in Harvard Style

Wood R. and I. Olszewska J. (2012). LIGHTING-VARIABLE ADABOOST BASED-ON SYSTEM FOR ROBUST FACE DETECTION . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2012) ISBN 978-989-8425-89-8, pages 494-497. DOI: 10.5220/0003888304940497


in Bibtex Style

@conference{mpbs12,
author={R. Wood and J. I. Olszewska},
title={LIGHTING-VARIABLE ADABOOST BASED-ON SYSTEM FOR ROBUST FACE DETECTION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2012)},
year={2012},
pages={494-497},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003888304940497},
isbn={978-989-8425-89-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2012)
TI - LIGHTING-VARIABLE ADABOOST BASED-ON SYSTEM FOR ROBUST FACE DETECTION
SN - 978-989-8425-89-8
AU - Wood R.
AU - I. Olszewska J.
PY - 2012
SP - 494
EP - 497
DO - 10.5220/0003888304940497