International Journal of Computer Vision

, Volume 41, Issue 1–2, pp 85–107 | Cite as

Coarse-to-Fine Face Detection

  • Francois Fleuret
  • Donald Geman


We study visual selection: Detect and roughly localize all instances of a generic object class, such as a face, in a greyscale scene, measuring performance in terms of computation and false alarms. Our approach is sequential testing which is coarse-to-fine in both in the exploration of poses and the representation of objects. All the tests are binary and indicate the presence or absence of loose spatial arrangements of oriented edge fragments. Starting from training examples, we recursively find larger and larger arrangements which are “decomposable,” which implies the probability of an arrangement appearing on an object decays slowly with its size. Detection means finding a sufficient number of arrangements of each size along a decreasing sequence of pose cells. At the beginning, the tests are simple and universal, accommodating many poses simultaneously, but the false alarm rate is relatively high. Eventually, the tests are more discriminating, but also more complex and dedicated to specific poses. As a result, the spatial distribution of processing is highly skewed and detection is rapid, but at the expense of (isolated) false alarms which, presumably, could be eliminated with localized, more intensive, processing.

visual selection face detection pose decomposition coarse-to-fine search sequential testing 


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  1. Amit, Y. 2000. A neural network architecture for visual selection. Neural Computation, 12:1059-1082.Google Scholar
  2. Amit, Y. and Geman, D. 1997. Shape quantization and recognition with randomized trees. Neural Computation, 9:1545-1588.Google Scholar
  3. Amit, Y. and Geman, D. 1999. A computational model for visual selection. Neural Computation, 11:1691-1715.Google Scholar
  4. Baum, E.B. and Haussler, D. 1989. What size net gives valid generalization? Neural Comp., 1:151-160.Google Scholar
  5. Cootes, T.F. and Taylor, C.J. 1996. Locating faces using statistical feature detectors. In Proceedings, Second International Conference on Automatic Face and Gesture Recognition, IEEE Computer Society Press, pp. 204-209.Google Scholar
  6. Devroye, L., Gyorfi, L., and Lugosi, G. 1995. Probabilistic Methods for Pattern Recognition. Springer-Verlag: Berlin.Google Scholar
  7. Fleuret, F. 2000. Détection hiérarchique de visages par apprentissage statistique. Ph.D. Thesis, University of Paris VI, Jussieu, France.Google Scholar
  8. Geman, D. and Jedynak, B. 1996. An active testing model for tracking roads from satellite images. IEEE Trans. PAMI, 18:1-15.Google Scholar
  9. Grimson, W.E.L. 1990. Object Recognition by Computer: The Role of Geometric Constraints. MIT Press: Cambridge, Massachusetts.Google Scholar
  10. Haiyuan, W., Qian, C., and Masahiko, Y. 1999. Face detection from color images using a fuzzy pattern matching method. IEEE Trans. PAMI, 10.Google Scholar
  11. Jedynak, B. and Fleuret, F. 1996. FrReconaissance d'objets 3d à l'aide d'arbres de classification. In Proc. Image' Com 96, Bordeaux, France.Google Scholar
  12. Lamdan, Y., Schwartz, J.T., and Wolfson, H.J. 1988. Object recognition by affine invariant matching. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 335-344.Google Scholar
  13. Leung, T., Burl, M., and Perona, P. 1995. Finding faces in cluttered scenes using labeled random graph matching. In Proceedings, 5th Int. Conf. on Comp. Vision, pp. 637-644.Google Scholar
  14. Maurer, T. and von der Malsburg, C. 1996. Tracking and learning graphs and pose on image sequences of faces. In Proceedings, Second International Conference on Automatic Face and Gesture Recognition, IEEE Computer Society Press, pp. 176-181.Google Scholar
  15. Miao, J., Yin, B., Wang, K., Shen, L., and Chen, X. 1999. A hierarchical multiscale and multiangle system for human face detection in complex background using gravity-center template. Pattern Recognition, 32:1237-1248.Google Scholar
  16. Ming, X. and Akatsuka, T. 1998. Multi-module method for detection of a human face from complex backgrounds. In Proceedings of the SPIE, pp. 793-802.Google Scholar
  17. Osuna, E., Freund, R., and Girosi, F. 1997. Training support vector machines:An application to face detection. In Proceedings,CVPR, IEEE Computer Society Press, pp. 130-136.Google Scholar
  18. Rojer, A.S. and Schwartz, E.L. 1992. A quotient space hough transform for space variant visual attention. In Neural Networks for Vision and Image Processing, G.A. Carpenter and S. Grossberg (Eds.), MIT Press: Cambridge, MA.Google Scholar
  19. Rowley, A.R. 1999. Neural network-based face detection. Ph.D. Thesis, Carnegie Mellon University, Pittsburgh, Pennsylvania.Google Scholar
  20. Rowley, H.A., Baluja, S., and Kanade, T. 1998. Neural networkbased face detection. IEEE Trans. PAMI, 20:23-38.Google Scholar
  21. Sabert, E. and Tekalp, A.M. 1998. Frontal-view face detection and facial feature extraction using color, shape, and symmetry-based cost functions. IEEE Trans. PAMI, 19:669-680.Google Scholar
  22. Sung, K.K. and Poggio, T. 1998. Example-based learning for viewbased face detection. IEEE Trans. PAMI, 20:39-51.Google Scholar
  23. Ullman, S. 1996. High-Level Vision. M.I.T. Press: Cambridge, MA.Google Scholar
  24. Vapnik, V. 1996. The Nature of Statistical Learning. Springer-Verlag: Berlin.Google Scholar
  25. Wee, S., Ji, S., Yoon, C., and Park, M. 1998. Face detection using pattern information and deformable template in motion images. In Proc. Fifth Inter. Conf. on Soft Computing and Information/ Intelligent Systems, pp. 213-216.Google Scholar
  26. Wilder, K. 1998. Decision tree algorithms for handwritten digit recognition. Ph.D. Thesis, University of Massachusetts, Amherst, Massachusetts.Google Scholar
  27. Yuille, A.L., Cohen, D.S., and Hallinan, P. 1992. Feature extraction from faces using deformable templates. Inter. J. Comp. Vision, 8:104-109.Google Scholar
  28. Zhu, S.C., Wu, Z.N., and Mumford, D. 1997. Minimax entropy principle and its application to texture modeling. Neural Computation, 9:1627-1660.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Francois Fleuret
    • 1
  • Donald Geman
    • 2
  1. 1.Avant-Projet IMEDIAINRIA-Rocquencourt, Domaine de VoluceauLe ChesnayFrance
  2. 2.Department of Mathematics and StatisticsUniversity of MassachusettsAmherstUSA

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