PARTS-BASED FACE DETECTION AT MULTIPLE VIEWS

Andreas Savakis, David Higgs

Abstract

This paper presents a parts-based approach to face detection, that is intuitive, easy to implement and can be used in conjunction with other image understanding operations that use prominent facial features. Artificial neural networks are trained as view-specific parts detectors for the eyes, mouth and nose. Once these salient facial features are identified, results for each view are integrated through a Bayesian network in order to reach the final decision. System performance is comparable to other state-of the art face detection methods while providing support for different view angles and robustness to partial occlusions.

References

  1. Fergus R., Perona P., and Zisserman A., 2003. Object Class Recognition by Unsupervised Scale-Invariant Learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  2. Heckerman D., 1995. A Tutorial on Learning With Bayesian Networks. Technical Report MSR-TR-95- 06, Microsoft Research, Advanced Technology Division.
  3. Hjelmas, E. and Low, B. K., 2001. Face Detection: A Survey. In Computer Vision and Image Understanding, vol. 83, pp. 236-274.
  4. Hsu R. L., Abdel-Mottaleb M., and Jain A. K., 2002. Face Detection in Color Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 696-706..
  5. Osuna E., Freund R., and Girosi F., 1997. Training Support Vector Machines: an Application to Face Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 130- 136.
  6. Phillips P. J., Moon H., Rizvi S. A., and Rauss P. J., 2000. The FERET Evaluation Methodology for FaceRecognition Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 090-1104, October.
  7. Pontil M. and Verri A., 1998. Support Vector Machines for 3D Object Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, pp. 637-646.
  8. Rowley H. A., Baluja S., and Kanade T., 1998. Neural Network-Based Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, pp. 23-38.
  9. Schneiderman, H. and Kanade T., 2000. A Statistical Method for 3D Object Detection Applied to Faces and Cars. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1746-1759.
  10. Schneiderman H. and Kanade T., 2004. Object Detection Using the Statistics of Parts. International Journal of Computer Vision, vol. 56, pp. 151-177.
  11. Sung K. K. and Poggio T., 1998. Example-based Learning for View-based Human Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, pp. 39-51.
  12. Viola P. A. and Jones M. J., 2001. Rapid Object Detection using a Boosted Cascade of Simple Features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511-518.
  13. Weber M., Welling M., and Perona P., 2000. Unsupervised Learning of Models for Recognition. In Proceedings of the European Conference on Computer Vision, vol. 1, pp. 18-32.
  14. Xiao R., Zhu L., and Zhang H. J., 2003. Boosting Cascade Learning for Object Detection. In IEEE International Conference on Computer Vision, ICCV03.
  15. Yang M. H., Roth D., and Ahuja N., 2000. Learning to Recognize 3D Objects with SNoW. In Proceedings of the European Conference on Computer Vision, vol. 1, pp. 439-454.
  16. Yow, K. C. and Cipolla R., 1997. Feature-based human face detection. Image and Vision Computing, vol. 15, pp. 713-735.
Download


Paper Citation


in Harvard Style

Savakis A. and Higgs D. (2007). PARTS-BASED FACE DETECTION AT MULTIPLE VIEWS . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 978-972-8865-74-0, pages 298-301. DOI: 10.5220/0002062202980301


in Bibtex Style

@conference{visapp07,
author={Andreas Savakis and David Higgs},
title={PARTS-BASED FACE DETECTION AT MULTIPLE VIEWS},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2007},
pages={298-301},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002062202980301},
isbn={978-972-8865-74-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - PARTS-BASED FACE DETECTION AT MULTIPLE VIEWS
SN - 978-972-8865-74-0
AU - Savakis A.
AU - Higgs D.
PY - 2007
SP - 298
EP - 301
DO - 10.5220/0002062202980301