Face Detection

Abstract

Face detection is the first step in automated face recognition. This chapter presents methods and algorithms for building face detectors. Focuses are on AdaBoost learning-based methods because they have been the most successful ones so far in terms of detection accuracy and speed. Effective postprocessing methods are also described. Experimental results are provided.

References

  1. 1.
    Alvira, M., Rifkin, R.: An empirical comparison of SNoW and svms for face detection. Technical Report AI Memo 2001-004 & CBCL Memo 193, MIT (2001) Google Scholar
  2. 2.
    Amit, Y., Geman, D., Wilder, K.: Joint induction of shape features and tree classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 19, 1300–1305 (1997) CrossRefGoogle Scholar
  3. 3.
    Baker, S., Nayar, S.: Pattern rejection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 544–549 (1996) Google Scholar
  4. 4.
    Bichsel, M., Pentland, A.P.: Human face recognition and the face image set’s topology. CVGIP, Image Underst. 59, 254–261 (1994) CrossRefGoogle Scholar
  5. 5.
    Bourdev, L.D., Brandt, J.: Robust object detection via soft cascade. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. II, pp. 236–243 (2005) Google Scholar
  6. 6.
    Brubaker, S.C., Wu, J., Sun, J., Mullin, M.D., Rehg, J.M.: On the design of cascades of boosted ensembles for face detection. Int. J. Comput. Vis. 77(1–3), 65–86 (2008) CrossRefGoogle Scholar
  7. 7.
    Crow, F.: Summed-area tables for texture mapping. In: SIGGRAPH, vol. 18(3), pp. 207–212 (1984) Google Scholar
  8. 8.
    Elad, M., Hel-Or, Y., Keshet, R.: Pattern detection using a maximal rejection classifier. Pattern Recognit. Lett. 23, 1459–1471 (2002) MATHCrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Fleuret, F., Geman, D.: Coarse-to-fine face detection. Int. J. Comput. Vis. 20, 1157–1163 (2001) Google Scholar
  11. 11.
    Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997) MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Technical report, Department of Statistics, Sequoia Hall, Stanford University, July 1998 Google Scholar
  13. 13.
    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
  14. 14.
    Huang, C., Ai, H., Li, Y., Lao, S.: High-performance rotation invariant multiview face detection. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 671–686 (2007) CrossRefGoogle Scholar
  15. 15.
    Küblbeck, C., Ernst, A.: Face detection and tracking in video sequences using the modified census transformation. Image Vis. Comput. 24(6), 564–572 (2006) CrossRefGoogle 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 CHI’99 Conference, Pittsburg, May 1999 Google Scholar
  17. 17.
    Li, S.Z., Zhang, Z.: FloatBoost learning and statistical face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1112–1123 (2004) CrossRefGoogle Scholar
  18. 18.
    Li, S.Z., Zhang, Z.Q., Shum, H.-Y., Zhang, H.: FloatBoost learning for classification. In: Proceedings of Neural Information Processing Systems, Vancouver (2002) Google Scholar
  19. 19.
    Li, S.Z., Zhu, L., Zhang, Z.Q., Blake, A., Zhang, H., Shum, H.: Statistical learning of multi-view face detection. In: Proceedings of the European Conference on Computer Vision, vol. 4, pp. 67–81, Copenhagen, Denmark, 28 May–2 June 2002 Google Scholar
  20. 20.
    Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: International Conference on Biometrics, pp. 828–837 (2007) Google Scholar
  21. 21.
    Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. MRL Technical Report, Intel Labs, December 2002 Google Scholar
  22. 22.
    Liu, C., Shum, H.-Y.: Kullback–Leibler boosting. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. I, pp. 587–594 (2003) Google Scholar
  23. 23.
    Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Trans. Pattern Anal. Mach. Intell. 7, 696–710 (1997) CrossRefGoogle Scholar
  24. 24.
    Osuna, E., Freund, R., Girosi, F.: Training support vector machines: An application to face detection. In: CVPR, pp. 130–136 (1997) Google Scholar
  25. 25.
    Papageorgiou, C.P., Oren, M., Poggio, T.: A general framework for object detection. In: Proceedings of IEEE International Conference on Computer Vision, pp. 555–562, Bombay (1998) Google Scholar
  26. 26.
    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, pp. 84–91 (1994) Google Scholar
  27. 27.
    Pham, M.-T., Cham, T.-J.: Fast training and selection of Haar features using statistics in boosting-based face detection. In: Proceedings of IEEE International Conference on Computer Vision (2007) Google Scholar
  28. 28.
    Pham, M.-T., Hoang, V.-D.D., Cham, T.-J.: Detection with multi-exit asymmetric boosting. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2008) Google Scholar
  29. 29.
    Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognit. Lett. 15(11), 1119–1125 (1994) CrossRefGoogle Scholar
  30. 30.
    Rowley, H., Baluja, S., Kanade, T.: Rotation invariant neural network-based face detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1998) Google Scholar
  31. 31.
    Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23–28 (1998) CrossRefGoogle Scholar
  32. 32.
    Schapire, R., Freund, Y., Bartlett, P., Lee, W.S.: Boosting the margin: A new explanation for the effectiveness of voting methods. Ann. Stat. 26(5), 1651–1686 (1998) MathSciNetMATHCrossRefGoogle Scholar
  33. 33.
    Schneiderman, H.: A statistical approach to 3D object detection applied to faces and cars (CMU-RI-TR-00-06). PhD thesis, RI (2000) Google Scholar
  34. 34.
    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
  35. 35.
    Schneiderman, H., Kanade, T.: Object detection using the statistics of parts. Int. J. Comput. Vis. 56(3), 151–177 (2004) CrossRefGoogle Scholar
  36. 36.
    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, vol. 11, pp. 571–577. MIT Press, Cambridge (1998) Google Scholar
  37. 37.
    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, New York (1998) Google Scholar
  38. 38.
    Sung, K.-K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 39–51 (1998) CrossRefGoogle Scholar
  39. 39.
    Tieu, K., Viola, P.: Boosting image retrieval. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 228–235 (2000) Google Scholar
  40. 40.
    Turk, M.: A random walk through eigenspace. IEICE Trans. Inf. Syst. E84-D(12), 1586–1695 (2001) Google Scholar
  41. 41.
    Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991) CrossRefGoogle Scholar
  42. 42.
    Various Face Detection Databases. www.ri.cmu.edu/projects/project_419.html
  43. 43.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii (2001) Google Scholar
  44. 44.
    Viola, P.A., Jones, M.J.: Fast and robust classification using asymmetric AdaBoost and a detector cascade. In: Advances in Neural Information Processing Systems, vol. 14, pp. 1311–1318 (2001) Google Scholar
  45. 45.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004) CrossRefGoogle Scholar
  46. 46.
    Wiskott, L., Fellous, J., Kruger, N., v. d. Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 775–779 (1997) CrossRefGoogle Scholar
  47. 47.
    Wu, J., Brubaker, S.C., Mullin, M.D., Rehg, J.M.: Fast asymmetric learning for cascade face detection. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 369–382 (2008) CrossRefGoogle Scholar
  48. 48.
    Xiao, R., Zhu, L., Zhang, H.J.: Boosting chain learning for object detection. In: Proceedings of IEEE International Conference on Computer Vision, pp. 709–714 (2003) CrossRefGoogle Scholar
  49. 49.
    Xiao, R., Zhu, H., Sun, H., Tang, X.: Dynamic cascades for face detection. In: Proceedings of IEEE International Conference on Computer Vision (2007) Google Scholar
  50. 50.
    Yang, M.-H., Kriegman, D., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 34–58 (2002) CrossRefGoogle Scholar
  51. 51.
    Yang, M.-H., Roth, D., Ahuja, N.: A SNoW-based face detector. In: Proceedings of Neural Information Processing Systems, pp. 855–861 (2000) Google Scholar
  52. 52.
    Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z.: Face detection based on multi-block LBP representation. In: International Conference on Biometrics, pp. 11–18 (2007) Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore

Personalised recommendations