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Face Detection

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Handbook of Face Recognition

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.

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Notes

  1. 1.

    The reader is referred to a review article [50] for other earlier face detection methods.

  2. 2.

    Viola and Jones [43, 45] used h m (x)∈{0,1}. Our notation is slightly different from but equivalent to theirs.

  3. 3.

    The modified Census Transform feature [15] is also used for face detection, and is very similar to LBP.

References

  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. Amit, Y., Geman, D., Wilder, K.: Joint induction of shape features and tree classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 19, 1300–1305 (1997)

    Article  Google Scholar 

  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. Bichsel, M., Pentland, A.P.: Human face recognition and the face image set’s topology. CVGIP, Image Underst. 59, 254–261 (1994)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  7. Crow, F.: Summed-area tables for texture mapping. In: SIGGRAPH, vol. 18(3), pp. 207–212 (1984)

    Google Scholar 

  8. Elad, M., Hel-Or, Y., Keshet, R.: Pattern detection using a maximal rejection classifier. Pattern Recognit. Lett. 23, 1459–1471 (2002)

    Article  MATH  Google Scholar 

  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. Fleuret, F., Geman, D.: Coarse-to-fine face detection. Int. J. Comput. Vis. 20, 1157–1163 (2001)

    Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Li, S.Z., Zhang, Z.: FloatBoost learning and statistical face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1112–1123 (2004)

    Article  Google Scholar 

  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. 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. 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. 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. 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. Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Trans. Pattern Anal. Mach. Intell. 7, 696–710 (1997)

    Article  Google Scholar 

  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. 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. 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. 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. 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. Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognit. Lett. 15(11), 1119–1125 (1994)

    Article  Google Scholar 

  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. Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23–28 (1998)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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. Schneiderman, H., Kanade, T.: Object detection using the statistics of parts. Int. J. Comput. Vis. 56(3), 151–177 (2004)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. Turk, M.: A random walk through eigenspace. IEICE Trans. Inf. Syst. E84-D(12), 1586–1695 (2001)

    Google Scholar 

  41. Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  42. Various Face Detection Databases. www.ri.cmu.edu/projects/project_419.html

  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. 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. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. Yang, M.-H., Kriegman, D., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 34–58 (2002)

    Article  Google Scholar 

  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. 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 

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Acknowledgements

This work was partially supported by the Chinese National Natural Science Foundation Project #61070146, the National Science and Technology Support Program Project #2009BAK43B26, and the AuthenMetric R&D Funds (2004–2011). The work was also partially supported by the TABULA RASA project (http://www.tabularasa-euproject.org) under the Seventh Framework Programme for research and technological development (FP7) of the European Union (EU), grant agreement #257289.

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Authors and Affiliations

  1. Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Stan Z. Li

  2. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Jianxin Wu

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  1. Stan Z. Li
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  2. Jianxin Wu
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Correspondence to Stan Z. Li .

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Editors and Affiliations

  1. Institute of Automation, Center Biometrics Research & Security, Chinese Academy of Science, Room 1227, Zhongguancun 95, Beijing Donglu, 100080, China, People's Republic

    Stan Z. Li

  2. Dept. Computer Science & Engineering, Michigan State University, East Lansing, 48824-1226, Michigan, USA

    Anil K. Jain

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Li, S.Z., Wu, J. (2011). Face Detection. In: Li, S., Jain, A. (eds) Handbook of Face Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-932-1_11

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  • DOI: https://doi.org/10.1007/978-0-85729-932-1_11

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