Statistical Learning of Multi-view Face Detection

  • Stan Z. Li
  • Long Zhu
  • ZhenQiu Zhang
  • Andrew Blake
  • HongJiang Zhang
  • Harry Shum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2353)


A new boosting algorithm, called FloatBoost, is proposed to overcome the monotonicity problem of the sequential AdaBoost learning. AdaBoost [1,2] is a sequential forward search procedure using the greedy selection strategy. The premise offered by the sequential procedure can be broken-down when the monotonicity assumption, i.e. that when adding a new feature to the current set, the value of the performance criterion does not decrease, is violated. FloatBoost incorporates the idea of Floating Search [3] into AdaBoost to solve the non-monotonicity problem encountered in the sequential search of AdaBoost.

We then present a system which learns to detect multi-view faces using FloatBoost. The system uses a coarse-to-fine, simple-to-complex architecture called detector-pyramid. FloatBoost learns the component detectors in the pyramid and yields similar or higher classification accuracy than AdaBoost with a smaller number of weak classifiers. This work leads to the first real-time multi-view face detection system in the world. It runs at 200 ms per image of size 320×240 pixels on a Pentium-III CPU of 700 MHz. A live demo will be shown at the conference.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Freund, Y., Schapire, R.: “A decision-theoretic generalization of on-line learning and an application to boosting”. Journal of Computer and System Sciences 55 (1997) 119–139MathSciNetMATHCrossRefGoogle Scholar
  2. 2.
    Schapire, R.E., Singer, Y.: “Improved boosting algorithms using confidence-rated predictions”. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory. (1998) 80–91Google Scholar
  3. 3.
    Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters 15 (1994) 1119–1125CrossRefGoogle Scholar
  4. 4.
    Schapire, R., Freund, Y., Bartlett, P., Lee, W.S.: “Boosting the margin: A new explanation for the effectiveness of voting methods”. The Annals of Statistics 26 (1998) 1651–1686MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Stearns, S.D.: “On selecting features for pattern classifiers”. In: Proceedings of International Conference Pattern Recognition. (1976) 71–75Google Scholar
  6. 6.
    Kittler, J.: “Feature set search algorithm”. In Chen, C.H., ed.: Pattern Recognition in Practice. NorthHolland, Sijthoff and Noordhoof (1980) 41–60Google Scholar
  7. 7.
    Jain, A., Zongker, D.: Feature selection: evaluation, application, and samll sample performance. IEEE Trans. on PAMI 19 (1997) 153–158CrossRefGoogle Scholar
  8. 8.
    Somol, P., Pudil, P., Novoviova, J., Paclik, P.: “Adaptive floating search methods in feature selection”. Pattern Recognition Letters 20 (1999) 1157–1163CrossRefGoogle Scholar
  9. 9.
    Rowley, H.A., Baluja, S., Kanade, T.: “Neural network-based face detection”. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (1998) 23–28CrossRefGoogle Scholar
  10. 10.
    Sung, K.K., Poggio, T.: “Example-based learning for view-based human face detection”. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (1998) 39–51CrossRefGoogle Scholar
  11. 11.
    Osuna, E., Freund, R., Girosi, F.: “Training support vector machines: An application to face detection”. In: CVPR. (1997) 130–136Google Scholar
  12. 12.
    Roth, D., Yang, M., Ahuja, N.: “A snow-based face detector”. In: Proceedings of Neural Information Processing Systems. (2000)Google Scholar
  13. 13.
    Bichsel, M., Pentland, A.P.: “Human face recognition and the face image set’s topology”. CVGIP: Image Understanding 59 (1994) 254–261CrossRefGoogle Scholar
  14. 14.
    Simard, P.Y., Cun, Y.A.L., Denker, J.S., Victorri, B.: “Transformation invariance in pattern recognition-tangent distance and tangent propagation”. In Orr, G.B., Muller, K.R., eds.: Neural Networks: Tricks of the Trade, Springer (1998)Google Scholar
  15. 15.
    Viola, P., Jones, M.: “Robust real time object detection”. In: IEEE ICCV Workshop on Statistical and Computational Theories of Vision, Vancouver, Canada (2001)Google Scholar
  16. 16.
    Kuchinsky, A., Pering, C., Creech, M.L., Freeze, D., Serra, B., Gwizdka, J.: ”FotoFile: A consumer multimedia organization and retrieval system”. In: Proceedings of ACM SIG CffI’99 Conference, Pittsburg (1999)Google Scholar
  17. 17.
    Pentland, A.P., Moghaddam, B., Starner, T.: “View-based and modular eigenspaces for face recognition”. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. (1994) 84–91Google Scholar
  18. 18.
    Feraud, J., Bernier, O., Collobert, M.: “A fast and accurate face detector for indexation of face images”. In: Proc. Fourth IEEE Int. Conf on Automatic Face and Gesture Recognition, Grenoble (2000)Google Scholar
  19. 19.
    Wiskott, L., Fellous, J., Kruger, N., malsburg, C.V.: ”face recognition by elastic bunch graph matching”. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 775–779CrossRefGoogle Scholar
  20. 20.
    Gong, S., McKenna, S., Collins, J.: “An investigation into face pose distribution”. In: Proc. IEEE International Conference on Face and Gesture Recognition, Vermont (1996)Google Scholar
  21. 21.
    Ng, J., Gong, S.: “performing multi-view face detection and pose estimation using a composite support vector machine across the view sphere”. In: Proc. IEEE International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, Corfu, Greece (1999) 14-21Google Scholar
  22. 22.
    Li, Y.M., Gong, S.G., Liddell, H.: “support vector regression and classification based multi-view face detection and recognition”. In: IEEE Int. Conf. Oo Face & Gesture Recognition, France (2000) 300–305Google Scholar
  23. 23.
    Huang, J., Shao, X., Wechsler, H.: “Face pose discrimination using support vector machines (SVM)”. In: Proceedings of International Conference Pattern Recognition, Brisbane, Queensland, Australia (1998)Google Scholar
  24. 24.
    Schneiderman, H., Kanade, T.: “A statistical method for 3d object detection applied to faces and cars”. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. (2000)Google Scholar
  25. 25.
    Friedman, J., Hastie, T., Tibshirani, R.: “Additive logistic regression: a statistical view of boosting”. Technical report, Department of Statistics, Sequoia Hall, Stanford Univerity (1998)Google Scholar
  26. 26.
    Amit, Y.,, Geman, D., Wilder, K.: “Joint induction of shape features and tree classifiers”. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 1300–1305CrossRefGoogle Scholar
  27. 27.
    Fleuret, F., Geman, D.: “Coarse-to-fine face detection”. International Journal of Computer Vision 20 (2001) 1157–1163Google Scholar
  28. 28.
    Papageorgiou, C.P., Oren, M., Poggio, T.: “A general framework for object detection”. In: Proceedings of IEEE International Conference on Computer Vision, Bombay, India (1998) 555–562Google Scholar
  29. 29.
    Simard, P.Y., Bottou, L., Haffner, P., Cun, Y.L.: “Boxlets: a fast convolution algorithm for signal processing and neural networks”. In Kearns, M., Solla, S., Cohn, D., eds.: Advances in Neural Information Processing Systems. Volume 11., MIT Press (1998) 571–577Google Scholar
  30. 30.
    Crow, F.: “Summed-area tables for texture mapping”. In: SIGGGRAPH. Volume 18(3). (1984) 207–212CrossRefGoogle Scholar
  31. 31.
    Erik Hjelmas, B.K.L.: “Face detection: A survey”. Computer Vision and Image Understanding 3 (2001)Google Scholar
  32. 32.
    Fan, W., Stolfo, S., Zhang, J.: “The application of adaboost for distributed, scalable and on-line learning”. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Stan Z. Li
    • 1
  • Long Zhu
    • 1
  • ZhenQiu Zhang
    • 2
  • Andrew Blake
    • 3
  • HongJiang Zhang
    • 1
  • Harry Shum
    • 1
  1. 1.Microsoft Research AisaBeijingChina
  2. 2.Institute of AutomationChinese Academy SinicaBeijingChina
  3. 3.Microsoft Research CambridgeCambradgeUK

Personalised recommendations