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FloatBoost Learning and Statistical Face Detection
September 2004 (vol. 26 no. 9)
pp. 1112-1123
A novel learning procedure, called FloatBoost, is proposed for learning a boosted classifier for achieving the minimum error rate. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithms. A second contribution of the paper is a novel statistical model for learning best weak classifiers using a stagewise approximation of the posterior probability. These novel techniques lead to a classifier which requires fewer weak classifiers than AdaBoost yet achieves lower error rates in both training and testing, as demonstrated by extensive experiments. Applied to face detection, the FloatBoost learning method, together with a proposed detector pyramid architecture, leads to the first real-time multiview face detection system reported.

[1] Y. Freund and R. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application To Boosting J. Computer and System Sciences, vol. 55, no. 1, pp. 119-139, Aug. 1997.
[2] L. Valiant, A Theory of the Learnable Comm. ACM, vol. 27, no. 11, pp. 1134-1142, 1984.
[3] M.J. Kearns and U. Vazirani, An Introduction to Computational Learning Theory. Cambridge, Mass.: MIT Press, 1994.
[4] R.E. Schapire and Y. Singer, Improved Boosting Algorithms Using Confidence-Rated Predictions Proc. 11th Ann. Conf. Computational Learning Theory, pp. 80-91, 1998.
[5] L. Breiman, Arcing Classifiers The Annals of Statistics, vol. 26, no. 3, pp. 801-849, 1998.
[6] R. Schapire, Y. Freund, P. Bartlett, and W.S. Lee, Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods The Annals of Statistics, vol. 26, no. 5, pp. 1651-1686, Oct. 1998.
[7] J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine The Annals of Statistics, vol. 29, no. 5, Oct. 2001.
[8] L. Mason, J. Baxter, P. Bartlett, and M. Frean, Functional Gradient Techniques for Combining Hypotheses Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, eds., pp. 221-247, Cambridge, Mass.: MIT Press, 1999.
[9] R. Zemel and T. Pitassi, A Gradient-Based Boosting Algorithm for Regression Problems Advances in Neural Information Processing Systems, vol. 13, 2001.
[10] J. Friedman, T. Hastie, and R. Tibshirani, Additive Logistic Regression: A Statistical View of Boosting The Annals of Statistics, vol. 28, no. 2, pp. 337-374, Apr. 2000.
[11] P. Buhlmann and B. Yu, Invited Discussion on `Additive Logistic Regression: A Statistical View of Boosting (friedman, hastie and tibshirani)' The Annals of Statistics, vol. 28, no. 2, pp. 377-386, Apr. 2000.
[12] P. Pudil, J. Novovicova, and J. Kittler, Floating Search Methods in Feature Selection Pattern Recognition Letters, vol. 15, no. 11, pp. 1119-1125, 1994.
[13] S.Z. Li, L. Zhu, Z.Q. Zhang, A. Blake, H. Zhang, and H. Shum, Statistical Learning of Multi-View Face Detection Proc. European Conf. Computer Vision, vol. 4, pp. 67-81, 2002.
[14] S.Z. Li, Z.Q. Zhang, H.-Y. Shum, and H. Zhang, FloatBoost Learning for Classification Proc. Neural Information Processing Systems, Dec. 2002.
[15] M. Bichsel and A.P. Pentland, Human Face Recognition and the Face Image Set's Topology CVGIP: Image Understanding, vol. 59, pp. 254-261, 1994.
[16] P.Y. Simard, Y.A.L. Cun, J.S. Denker, and B. Victorri, Transformation Invariance in Pattern Recognition Tangent Distance and Tangent Propagation Neural Networks: Tricks of the Trade, G.B. Orr and K.-R. Muller, eds., Springer, 1998.
[17] H. Rowley, S. Baluja, and T. Kanade, "Neural Network-Based Face Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, Jan. 1998, pp. 23-38.
[18] K.K. Sung and T. Poggio, "Example-Based Learning for View-Based Human Face Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 39-50, Jan. 1998.
[19] E. Osuna, R. Freund, and F. Girosi, Training Support Vector Machines: An Application to Face Detection Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 17-19, 1997.
[20] M.-H. Yang, D. Roth, and N. Ahuja, A SNoW-Based Face Detector Proc. Neural Information Processing Systems, pp. 855-861, 2000.
[21] Handbook of Face Recognition, S.Z. Li and A.K. Jain, eds. Springer-Verlag, (in press), 2004.
[22] P. Viola and M. Jones, Robust Real Time Object Detection IEEE ICCV Workshop Statistical and Computational Theories of Vision, July 2001.
[23] P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple Features Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001.
[24] K. Tieu and P. Viola, “Boosting Image Retrieval,” Proc. Computer Vision and Pattern Recognition, pp. pp. 228-235, 2000.
[25] H. Schneiderman, A Statistical Approach to 3D Object Detection Applied to Faces and Cars (cmu-ri-tr-00-06) PhD dissertation, R.I., 2000.
[26] C. Liu, A Bayesian Discriminating Features Method for Face Detection IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 6, pp. 725-740, June 2003.
[27] B. Moghaddam and A. Pentland, “Probabilistic Visual Learning for Object Representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 696-710, July 1997.
[28] A. Kuchinsky, C. Pering, M.L. Creech, D. Freeze, B. Serra, and J. Gwizdka, FotoFile: A Consumer Multimedia Organization and Retrieval System Proc. ACM SIG CHI'99 Conf., May 1999.
[29] A. Pentland, B. Moghaddam, and Starner, "View-Based and Modular Eigenspaces for Face Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1994, pp. 84-91.
[30] J. Feraud, O. Bernier, and M. Collobert, A Fast and Accurate Face Detector for Indexation of Face Images Proc. Fourth IEEE Int'l Conf. Automatic Face and Gesture Recognition, 2000.
[31] L. Wiskott, J.M. Fellous, N. Kruger, and C. von der Malsburg, Face Recognition by Elastic Bunch Graph Matching IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775-779, July 1997.
[32] S. Gong, S. McKenna, and J. Collins, An Investigation into Face Pose Distribution Proc. IEEE Int'l Conf. Face and Gesture Recognition, 1996.
[33] J. Ng and S. Gong, Performing Multi-View Face Detection and Pose Estimation Using a Composite Support Vector Machine Across The View Sphere Proc. IEEE Int'l Workshop Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pp. 14-21, Sept. 1999.
[34] Y. Li, S. Gong, and H. Liddell, “Support Vector Regression and Classification Based Multi-View Face Detection and Recognition,” Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, pp. 300-305, 2000.
[35] J. Huang, X. Shao, and H. Wechsler, Face Pose Discrimination Using Support Vector Machines (SVM) Proc. Int'l Conf. Pattern Recognition, 1998.
[36] H. Schneiderman and T. Kanade, "A Statistical Method for 3D Object Detection Applied to Faces and Cars," Proc. IEEE Computer Vision and Pattern Recognition (CVPR 00), IEEE CS Press, 2000, pp. 746—751.
[37] H. Schneiderman and T. Kanade, Object Detection Using the Statistics of Parts Int'l J. Computer Vision, vol. 56, no. 3, pp. 151-177, Feb. 2004.
[38] S D. Stearns, On Selecting Features for Pattern Classifiers Proc. Int'l Conf. Pattern Recognition, pp. 71-75, 1976.
[39] J. Kittler, Feature Set Search Algorithm Pattern Recognition in Practice, C.H. Chen, ed., Sijthoff and Noordhoof: North Holland, pp. 41-60, 1980.
[40] A. Jain and D. Zongker, Feature Selection: Evaluation, Application, and Small Sample Performance IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 2, pp. 153-158, Feb. 1997.
[41] P. Somol, P. Pudil, J. Novoviova, and P. Paclik, Adaptive Floating Search Methods in Feature Selection Pattern Recognition Letters, vol. 20, pp. 1157-1163, 1999.
[42] C.P. Papageorgiou, M. Oren, and T. Poggio, A General Framework for Object Detection Proc. IEEE Int'l Conf. Computer Vision, pp. 555-562, 1998.
[43] P.Y. Simard, L. Bottou, P. Haffner, and Y.L. Cun, Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks Advances in Neural Information Processing Systems, M. Kearns, S. Solla, and D. Cohn, eds., vol. 11, MIT Press, pp. 571-577, 1998.
[44] F. Crow, Summed-Area Tables for Texture Mapping Proc. SIGGRAPH, vol. 18, no. 3, pp. 207-212, 1984.
[45] R. Lienhart and J. Maydt, An Extended Set of Haar-Like Features for Rapid Object Detection Proc. IEEE Int'l Conf. Image Processing, vol. 1, pp. 900-903, 2002.
[46] Y. Amit, D. Geman, and K. Wilder, “Joint Induction of Shape Features and Tree Classifiers,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 11, pp. 1300-1305, Nov. 1997.
[47] F. Fleuret and D. Geman, Coarse-to-Fine Face Detection Int'l J. Computer Vision, vol. 20, pp. 1157-1163, 2001.
[48] B.K.L. Erik Hjelmas, Face Detection: A Survey Computer Vision and Image Understanding, vol. 3, no. 3, pp. 236-274, Sept. 2001.

Index Terms:
Pattern classification, boosting learning, AdaBoost, FloatBoost, feature selection, statistical models, face detection.
Citation:
Stan Z. Li, ZhenQiu Zhang, "FloatBoost Learning and Statistical Face Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1112-1123, Sept. 2004, doi:10.1109/TPAMI.2004.68
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