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.
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Amit, Y. and Geman, D. 1999. A computational model for visual selection. Neural Computation, 11:1691–1715.
Crow, F. 1984. Summed-area tables for texture mapping. In Proceedings of SIGGRAPH, 18(3):207–212.
Fleuret, F. and Geman, D. 2001. Coarse-to-fine face detection. Int. J. Computer Vision, 41:85–107.
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.
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.
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.
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.
John, G., Kohavi, R., and Pfeger, K. 1994. Irrelevant features and the subset selection problem. In Machine Learning Conference Proceedings.
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.
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.
Papageorgiou, C., Oren, M., and Poggio, T. 1998. A general framework for object detection. In International Conference on Computer Vision.
Quinlan, J. 1986. Induction of decision trees. Machine Learning, 1:81–106.
Roth, D., Yang, M., and Ahuja, N. 2000. A snowbased face detector. In Neural Information Processing 12.
Rowley, H., Baluja, S., and Kanade, T. 1998. Neural network-based face detection. IEEE Patt. Anal. Mach. Intell., 20:22–38.
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.
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.
Schneiderman, H. and Kanade, T. 2000. A statistical method for 3D object detection applied to faces and cars. In International Conference on Computer Vision.
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.
Sung, K. and Poggio, T. 1998. Example-based learning for viewbased face detection. IEEE Patt. Anal. Mach. Intell., 20:39–51.
Tieu, K. and Viola, P. 2000. Boosting image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
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.
Webb, A. 1999. Statistical Pattern Recognition. Oxford University Press: New York.
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Viola, P., Jones, M.J. Robust Real-Time Face Detection. International Journal of Computer Vision 57, 137–154 (2004). https://doi.org/10.1023/B:VISI.0000013087.49260.fb
- face detection
- human sensing