Skip to main content

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. M. Alvira and R. Rifkin. An empirical comparison of SNoW and svms for face detection“. Technical Report AI Momo 2001–004 & CBCL Memo 193, MIT, 2001.

    Google Scholar 

  2. Y. Amit, D. Geman, and K. Wilder. Joint induction of shape features and tree classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:1300–1305, 1997.

    Article  Google Scholar 

  3. S. Baker and S. Nayar. Pattern rejection. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 544–549,1996.

    Google Scholar 

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

    Article  Google Scholar 

  5. F. Crow. Summed-area tables for texture mapping. In SIGGRAPH, volume 18(3), pages 207–212, 1984.

    Article  Google Scholar 

  6. M. Elad, Y. Hel-Or, and R. Keshet. Pattern detection using a maximal rejection classifier. Pattern Recognition Letters, 23:1459–1471, 2002.

    Article  MATH  Google Scholar 

  7. J. Feraud, O. Bernier, and M. Collobert. 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 

  8. F. Fleuret and D. Geman. Coarse-to-fine face detection. International Journal of Computer Vision, 20:1157–1163, 2001.

    Google Scholar 

  9. Y. Freund and R. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119–139, August 1997.

    Article  MathSciNet  MATH  Google Scholar 

  10. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. Technical report, Department of Statistics, Sequoia Hall, Stanford Univerity, July 1998.

    Google Scholar 

  11. S. Gong, S. McKenna, and J. Collins. An investigation into face pose distribution. In Proc. IEEE International Conference on Face and Gesture Recognition, Vermont, 1996.

    Google Scholar 

  12. E. Hjelmas and B. K. Low. Face detection: A survey. Computer Vision and Image Understanding, 3(3):236–274, September 2001.

    Article  Google Scholar 

  13. R.-L. Hsu, M. Abdel-Mottaleb, and A. K. Jain. Face detection in color images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):696–706, 2002.

    Article  Google Scholar 

  14. J. Huang, X. Shao, and H. Wechsler. Face pose discrimination using support vector machines (SVM). In Proceedings of International Conference Pattern Recognition, Brisbane, Queensland, Australia, 1998.

    Google Scholar 

  15. A. Jain and D. Zongker. Feature selection: evaluation, application, and samll sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2):153–158, 1997.

    Article  Google Scholar 

  16. J. Kittler. Feature set search algorithm. In C. H. Chen, editor, Pattern Recognition in Practice, pages 41–60. NorthHolland, Sijthoff and Noordhoof, 1980.

    Google Scholar 

  17. A. Kuchinsky, C. Pering, M. L. Creech, D. Freeze, B. Serra, and J. Gwizdka. FotoFile: A consumer multimedia organization and retrieval system. In Proceedings of ACM SIG CHI’99 Conference, Pittsburg, May 1999.

    Google Scholar 

  18. S. Z. Li and Z. Zhang. FloatBoost learning and statistical face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9):1112–1123, 2004.

    Article  Google Scholar 

  19. S. Z. Li, Z. Q. Zhang, H.-Y. Shum, and H. Zhang. FloatBoost learning for classification. In Proceedings of Neural Information Processing Systems, Vancouver, 2002.

    Google Scholar 

  20. S. Z. Li, L. Zhu, Z. Q. Zhang, A. Blake, H. Zhang, and H. Shum. Statistical learning of multi-view face detection. In Proceedings of the European Conference on Computer Vision, volume 4, pages 67–81, Copenhagen, Denmark, May 28–June 2 2002.

    Google Scholar 

  21. Y. M. Li, S. G. Gong, and H. Liddell. Support vector regression and classification based multi-view face detection and recognition. In IEEE Int. Conf. Oo Face & Gesture Recognition, pages 300–305, Grenoble, 2000.

    Google Scholar 

  22. R. Lienhart, A. Kuranov, and V. Pisarevsky. Empirical analysis of detection cascades of boosted classifiers for rapid object detection. MRL Technical Report, Intel Labs, December 2002.

    Google Scholar 

  23. C. Liu. A Bayesian discriminating features method for face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(6):725–740, 2003.

    Article  Google Scholar 

  24. B. Martinkauppi. Face colour under varying illumination-analysis and applications. Ph.D. thesis, Department of Electrical and Information Engineering, University of Oulu, Finland, 2002.

    Google Scholar 

  25. B. Moghaddam and A. Pentland. Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:696–710, 1997.

    Article  Google Scholar 

  26. J. Ng and S. Gong. 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, pages 14–21, Corfu, 1999.

    Google Scholar 

  27. E. Osuna, R. Freund, and F. Girosi. Training support vector machines: An application to face detection. In CVPR, pages 130–136, 1997.

    Google Scholar 

  28. C. P. Papageorgiou, M. Oren, and T. Poggio. A general framework for object detection. In Proceedings of IEEE International Conference on Computer Vision, pages 555–562, Bombay, 1998.

    Google Scholar 

  29. A. P. Pentland, B. Moghaddam, and T. Starner. View-based and modular eigenspaces for face recognition. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 84–91, 1994.

    Google Scholar 

  30. P. Pudil, J. Novovicova, and J. Kittler. Floating search methods in feature selection. Pattern Recognition Letters, 15(11):1119–1125, 1994.

    Article  Google Scholar 

  31. H. Rowley, S. Baluja, and T. Kanade. Rotation invariant neural network-based face detection. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998.

    Google Scholar 

  32. H. A. Rowley, S. Baluja, and T. Kanade. Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1):23–28, 1998.

    Article  Google Scholar 

  33. R. Schapire, Y. Freund, P. Bartlett, and W. S. Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, 26(5):1651–1686, 1998.

    Article  MathSciNet  MATH  Google Scholar 

  34. H. Schneiderman. A Statistical Approach to 3D Object Detection Applied to Faces and Cars (CMURI-TR-00-06). PhD thesis, RI, 2000.

    Google Scholar 

  35. H. Schneiderman and T. Kanade. 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 

  36. H. Schneiderman and T. Kanade. Object detection using the statistics of parts. International Journal of Computer Vision, 56(3):151–177, Feb 2004.

    Article  Google Scholar 

  37. P. Y. Simard, L. Bottou, P. Haffner, and Y. L. Cun. Boxlets: a fast convolution algorithm for signal processing and neural networks. In M. Kearns, S. Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems, volume 11, pages 571–577. MIT Press, 1998.

    Google Scholar 

  38. P. Y. Simard, Y. A. L. Cun, J. S. Denker, and B. Victorri. Transformation invariance in pattern recognition — tangent distance and tangent propagation. In G. B. Orr and K.-R. Muller, editors, Neural Networks: Tricks of the Trade. Springer, New York, 1998.

    Google Scholar 

  39. P. Somol, P. Pudil, J. Novoviova, and P. Paclik. Adaptive floating search methods in feature selection. Pattern Recognition Letters, 20:1157–1163, 1999.

    Article  Google Scholar 

  40. S. D. Stearns. On selecting features for pattern classifiers. In Proceedings of International Conference Pattern Recognition, pages 71–75, 1976.

    Google Scholar 

  41. K.-K. Sung and T. Poggio. Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1):39–51, 1998.

    Article  Google Scholar 

  42. K. Tieu and P. Viola. Boosting image retrieval. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, pages 228–235, 2000.

    Google Scholar 

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

    Google Scholar 

  44. M. A. Turk and A. P. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71–86, 1991.

    Article  Google Scholar 

  45. Various. Face Detection Databases, www.ri.cmu.edu/projects/project 419.html.

    Google Scholar 

  46. P. Viola and M. Jones. 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, 12-14 2001.

    Google Scholar 

  47. P. Viola and M. Jones. Robust real time object detection. In IEEE ICCV Workshop on Statistical and Computational Theories of Vision, Vancouver, 2001.

    Google Scholar 

  48. L. Wiskott, J. Fellous, N. Kruger, and C. v. d. malsburg. Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):775–779, 1997.

    Article  Google Scholar 

  49. J. Yang, W. Lu, and A. Waibel. Skin-color modeling and adaptation. In Proceedings of the First Asian Conference on Computer Vision, pages 687–694, 1998.

    Google Scholar 

  50. M.-H. Yang and N. Ahuja. Gaussian mixture model for human skin color and its application in image and video databases. In Proc. of the SPIE Conf. on Storage and Retrieval for Image and Video Databases, volume 3656, pages 458–466, San Jose, 1999.

    Google Scholar 

  51. M.-H. Yang, N. Ahuja, and D. Kriegman. Face detection using mixtures of linear subspaces. In Proceedings of the 4-th IEEE International Conference on Automatic Face and Gesture Recognition, pages 70–76, Grenoble, 2000.

    Google Scholar 

  52. M.-H. Yang, D. Kriegman, and N. Ahuja. Detecting faces in images: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1):34–58, 2002.

    Article  Google Scholar 

  53. M.-H. Yang, D. Roth, and N. Ahuja. A SNoW-based face detector. In Proceedings of Neural Information Processing Systems, pages 855–861, 2000.

    Google Scholar 

  54. B. D. Zarit, B. J. Super, and F. K. H. Quek. Comparison of five color models in skin pixel classification. In IEEE ICCV Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-time Systems, pages 58–63, Corfu, Greece, September 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer Science+Business Media, Inc.

About this chapter

Cite this chapter

Li, S.Z. (2005). Face Detection. In: Handbook of Face Recognition. Springer, New York, NY. https://doi.org/10.1007/0-387-27257-7_2

Download citation

  • DOI: https://doi.org/10.1007/0-387-27257-7_2

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-40595-7

  • Online ISBN: 978-0-387-27257-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics