International Journal of Computer Vision

, Volume 57, Issue 2, pp 137–154 | Cite as

Robust Real-Time Face Detection

  • Paul Viola
  • Michael J. Jones
Article

Abstract

This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algorithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. A set of experiments in the domain of face detection is presented. The system yields face detection performance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per second.

face detection boosting human sensing 

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References

  1. Amit, Y. and Geman, D. 1999. A computational model for visual selection. Neural Computation, 11:1691–1715.Google Scholar
  2. Crow, F. 1984. Summed-area tables for texture mapping. In Proceedings of SIGGRAPH, 18(3):207–212.Google Scholar
  3. Fleuret, F. and Geman, D. 2001. Coarse-to-fine face detection. Int. J. Computer Vision, 41:85–107.Google Scholar
  4. Freeman, W.T. and Adelson, E.H. 1991. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(9):891–906.Google Scholar
  5. Freund, Y. and Schapire, R.E. 1995. A decision-theoretic generalization of on-line learning and an application to boosting. In Computational Learning Theory: Eurocolt 95, Springer-Verlag, pp. 23–37.Google Scholar
  6. Greenspan, H., Belongie, S., Gooodman, R., Perona, P., Rakshit, S., and Anderson, C. 1994. Overcomplete steerable pyramid filters and rotation invariance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
  7. Itti, L., Koch, C., and Niebur, E. 1998. A model of saliency-based visual attention for rapid scene analysis. IEEE Patt. Anal. Mach. Intell., 20(11):1254–1259.Google Scholar
  8. John, G., Kohavi, R., and Pfeger, K. 1994. Irrelevant features and the subset selection problem. In Machine Learning Conference Proceedings.Google Scholar
  9. Osuna, E., Freund, R., and Girosi, F. 1997a. Training support vector machines: An application to face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
  10. Osuna, E., Freund, R., and Girosi, F. 1997b. Training support vector machines: an application to face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
  11. Papageorgiou, C., Oren, M., and Poggio, T. 1998. A general framework for object detection. In International Conference on Computer Vision.Google Scholar
  12. Quinlan, J. 1986. Induction of decision trees. Machine Learning, 1:81–106.Google Scholar
  13. Roth, D., Yang, M., and Ahuja, N. 2000. A snowbased face detector. In Neural Information Processing 12.Google Scholar
  14. Rowley, H., Baluja, S., and Kanade, T. 1998. Neural network-based face detection. IEEE Patt. Anal. Mach. Intell., 20:22–38.Google Scholar
  15. Schapire, R.E., Freund, Y., Bartlett, P., and Lee, W.S. 1997. Boosting the margin: A new explanation for the effectiveness of voting methods. In Proceedings of the Fourteenth International Conference on Machine Learning.Google Scholar
  16. Schapire, R.E., Freund, Y., Bartlett, P., and Lee, W.S. 1998. Boosting the margin: A new explanation for the effectiveness of voting methods. Ann. Stat., 26(5):1651–1686.Google Scholar
  17. Schneiderman, H. and Kanade, T. 2000. A statistical method for 3D object detection applied to faces and cars. In International Conference on Computer Vision.Google Scholar
  18. Simard, P.Y., Bottou, L., Haffner, P., and LeCun, Y. (1999). Boxlets: A fast convolution algorithm for signal processing and neural networks. In M. Kearns, S. Solla, and D. Cohn (Eds.), Advances in Neural Information Processing Systems, vol. 11, pp. 571– 577.Google Scholar
  19. Sung, K. and Poggio, T. 1998. Example-based learning for viewbased face detection. IEEE Patt. Anal. Mach. Intell., 20:39–51.Google Scholar
  20. Tieu, K. and Viola, P. 2000. Boosting image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
  21. Tsotsos, J., Culhane, S., Wai, W., Lai, Y., Davis, N., and Nuflo, F. 1995. Modeling visual-attention via selective tuning. Artificial Intelligence Journal, 78(1/2):507–545.Google Scholar
  22. Webb, A. 1999. Statistical Pattern Recognition. Oxford University Press: New York.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Paul Viola
    • 1
  • Michael J. Jones
    • 2
  1. 1.Microsoft ResearchOne Microsoft WayRedmondUSA
  2. 2.Mitsubishi Electric Research LaboratoryCambridgeUSA

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