In this paper, we analysis the Viola-Jones algorithm, the most real-time face detection system has been used. It is consisting from three main concepts to enable a robust detection: the integral image for Haar feature computation, Adaboost for selecting feature and cascade to make resource allocation more efficient. Here we propose each stage starting from Integral image to the end with Cascading and some of algorithmic description for stages. The Viola-Jones algorithm gives multiple detections, a post-processing step which reduce detection redundancy using Adaboost and cascading.

[1]      P. Viola and M. Jones, “Managing work role performance: Challenges for twenty-first century organizations and their employees.,” Rapid Object Detect. using a Boost. Cascade Simple Featur., pp. 511–518, 2001.

[2]      Y.-Q. Wang, “An Analysis of the Viola-Jones Face Detection Algorithm,” Image Process. Line, vol. 4, pp. 128–148, 2014, doi: 10.5201/ipol.2014.104.

[3]      C. Papageorgiou, M. Oren, and T. Poggio. A general framework for object detection. In International Conference on Computer Wsion, 1998.

[4]      William T. Freeman and Edward H. Adelson. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(9):89 1-906, 1991.

[5]      H. Greenspan, S. Belongie, R. Gooodman, P. Perona, S. Rakshit, and C. Anderson. Overcomplete steerable pyramid fil

[6] Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. In Computational Learning Theory: Eurocolt ’95, pages 23–37. Springer-Verlag, 1995.

[7] H. Greenspan, S. Belongie, R. Gooodman, P. Perona, S. Rakshit, and C. Anderson. Overcomplete steerable pyramid filters and rotation invariance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1994.

[8] L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Patt. Anal. Mach. Intell., 20(11):1254–1259, November 1998.

[9] Edgar Osuna, Robert Freund, and Federico Girosi. Training support vector machines: an application to face detection. In Proceedings of the IEEE Conference on Computer Vision

and Pattern Recognition, 1997.

[10] C. Papageorgiou, M. Oren, and T. Poggio. A general framework for object detection. In International Conference on Computer Vision, 1998.

[11] D. Roth, M. Yang, and N. Ahuja. A snowbased face detector. In Neural Information Processing 12, 2000.

[12] H. Rowley, S. Baluja, and T. Kanade. Neural network-based face detection. In IEEE Patt. Anal. Mach. Intell., volume 20, pages 22–38, 1998.

[13] R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee. Boosting the margin: a new explanation for the effectiveness of voting methods. Ann. Stat., 26(5):1651–1686, 1998.

[14] Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. In Proceedings of the Fourteenth International Conference on Machine Learning,1997.

[15] H. Schneiderman and T. Kanade. A statistical method for 3D object detection applied to faces and cars. In International Conference on Computer Vision, 2000.

[16] K. Sung and T. Poggio. Example-based learning for viewbased face detection. In IEEE Patt. Anal. Mach. Intell., volume 20, pages 39–51, 1998.

[17] J.K. Tsotsos, S.M. Culhane,W.Y.K.Wai, Y.H. Lai, N. Davis, and F. Nuflo. Modeling visual-attention via selective tuning. Artificial Intelligence Journal, 78(1-2):507–545, October1995.

[18] Andrew Webb. Statistical Pattern Recognition. Oxford University Press, New York, 1999.  

[19] F. Crow. Summed-area tables for texture mapping. In Proceedings of SIGGRAPH, volume 18(3), pages 207–212, 1984.

[20] F. Fleuret and D. Geman. Coarse-to-fine face detection. Int.J. Computer Vision, 2001.