Skip to main content
SpringerLink
Log in

A unified learning framework for object detection and classification using nested cascades of boosted classifiers

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

In this paper a unified learning framework for object detection and classification using nested cascades of boosted classifiers is proposed. The most interesting aspect of this framework is the integration of powerful learning capabilities together with effective training procedures, which allows building detection and classification systems with high accuracy, robustness, processing speed, and training speed. The proposed framework allows us to build state of the art face detection, eyes detection, and gender classification systems. The performance of these systems is validated and analyzed using standard face databases (BioID, FERET and CMU-MIT), and a new face database (UCHFACE).

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Baluja S., Sahami M. and Rowley H.A. (2004). Efficient face orientation discrimination. International conference on image processing 1: 589–592

    Google Scholar 

  2. Brubaker, S.C., Mullin, M.D., Rehg, J.M.: Towards optimal training of cascaded detectors. ECCV 2006: 9th European conference on computer vision, Graz, Austria, 7–13 May, 2006, Proceedings, vol. 1, pp. 325–337, Lecture Notes in Computer Science 3951 (2006)

  3. Buchala, S., Davey, N., Frank, R.J., Gale, T.M., Loomes M., Kanargard, W.: Gender classification of face images: the role of global and feature-based information. ICONIP 2004, Calcutta, India, Lecture Notes in Computer Science 3316, pp. 763–768 (2004)

  4. Delakis M. and Garcia C. (2004). Convolutional face finder: a neural architecture for fast and robust face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26(11): 1408–1423

    Article  Google Scholar 

  5. Fasel I., Fortenberry B. and Movellan J. (2005). A generative framework for real time object detection and classification. Comput. Vision Image Understand. 98: 182–210

    Article  Google Scholar 

  6. Fröba, B., Ernst, A.: Face detection with the modified census transform. Sixth Int. Conf. on Face and Gesture Recognition, pp. 91–96 (2004)

  7. Fröba, B., Küblbeck, Ch.: Robust face detection at video frame rate based on edge orientation features. Fifth Int. Conf. on Automatic Face and Gesture Recognition, pp. 342–347 (2002)

  8. Hjelmås E. and Low B.K. (2001). Face detection: a survey. Comput. Vision Image Understand. 83: 236–274

    Article  MATH  Google Scholar 

  9. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the Hausdorff distance. Third Int. Conf. on Audio- and Video-based Biometric Person Authentication, Lecture Notes in Computer Science, LNCS-2091, pp. 90–95 (2001)

  10. Kirchberg, K.J., Jesorsky, O., Frischholz, R.W.: Genetic model optimization for Hausdorff distance-based face localization. Int. ECCV 2002 Workshop on Biometric Authentication, Lecture Notes In Computer Science; vol. 2359, pp. 103–111 (2002)

  11. Kölsch, M., Turk, M.: Robust Hand Detection. Sixth Int. Conf. on Face and Gesture Recognition, pp. 614–619 (2004)

  12. Li, S.Z., Zhu, L., Zhang, Z.Q., Blake, A., Zhang, H.J., Shum, H.: Statistical learning of multi-view face detection. Seventh Eu. Conf. on Computer Vision, Lecture Notes in Computer Science, vol. 2353, pp. 67–81 (2002)

  13. Lin, Y.-Y., Liu, T.-L., Fuh, C.-S.: Fast object detection with occlusion. Eighth European Conf. on Computer Vision, Lecture Notes in Computer Science 3021, pp. 402–413 (2004)

  14. Liu, C., Shum, H.-Y.: Kullback-leibler boosting. IEEE Conference of Computer Vision and Pattern Recognition, pp. 587–594 (2003)

  15. Ma, Y., Ding, X., Wang, Z., Wang, N.: Robust precise eye location under probabilistic framework. Sixth Int. Conf. on Face and Gesture Recognition, pp. 339–344 (2004)

  16. Moghaddam B. and Yang M.-H. (2002). Learning gender with support faces. IEEE Trans. Pattern Anal. Mach. Intell. 24(5): 707–711

    Article  Google Scholar 

  17. Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. IEEE Conference of Computer Vision and Pattern Recognition, pp. 130–136 (1997)

  18. Phillips P.J., Wechsler H., Huang J. and Rauss P. (1998). The FERET database and evaluation procedure for face recognition algorithms. Image Vision Comput. J. 16(5): 295–306

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Ruiz-del-Solar J. and Navarrete P. (2005). Eigenspace-based face recognition: a comparative study of different approaches. IEEE Trans. Syst. Man Cybernetics C (Special Issue on Biometric Systems) 35(3): 315–325

    Google Scholar 

  21. Schapire R. and Singer Y. (1999). Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3): 297–336

    Article  MATH  Google Scholar 

  22. Schneiderman, H.: Feature-centric evaluation for efficient cascade object detection. IEEE Conference of Computer Vision and Pattern Recognition, pp. 29–36 (2004)

  23. Schneiderman H. and Kanade T. (2000). A statistical model for 3D object detection applied to faces and cars. IEEE Conf. Comput. Vision and Pattern Recogn. 1: 746–751

    Google Scholar 

  24. Shakhnarovich, G., Viola, P., Moghaddam, B.: A unified learning framework for real time face detection & classification. Int Conf. on Automatic Face & Gesture Recognition, FG 2002, pp. 16–26 (2002)

  25. Sun, J., Rehg, J.M, Bobick, A.F.: Automatic cascade training with perturbation bias. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), vol. 2, pp. 276–283 (2004)

  26. Sung K. and Poggio T. (1998). Example-based learning for viewed-based human face deteccion. IEEE Trans. Pattern Anal. Mach. Intell. 20(1): 39–51

    Article  Google Scholar 

  27. Torralba, A., Murphy, K., Freeman, W.: Sharing visual features for multiclass and multiview object detection. AI Memo 2004-008, MIT, CSAIL Laboratory (2004)

  28. Verschae, R., Ruiz-del-Solar, J.: A hybrid face detector based on an asymmetrical adaboost cascade detector and a wavelet-Bayesian-detector. Lecture Notes in Computer Science 2686, Springer, Heidelberg pp. 742–749 (2003)

  29. Verschae, R., Ruiz-del-Solar, J., Köppen, M., Garcia, R.V.: Improvement of a face detection system by evolutionary multi-objective optimization. Fifth Int. Conf. on Hybrid Intelligent Systems, pp. 361–366 (2005)

  30. Verschae, R., Ruiz-del-Solar, J., Correa, M.: Gender classification of faces using adaboost. CIARP 2006. Lecture Notes in Computer Science. vol. 4225, 68–78 (2006)

  31. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 511–518 (2001)

  32. Viola P. and Jones M. (2002). Fast and robust classification using asymmetric adaboost and a detector cascade. Advances in Neural Information Processing System 14.. MIT Press, Cambridge

    Google Scholar 

  33. Viola P., Jones M. and Snow D. (2003). Detecting pedestrians using patterns of motion and appearance. Nineth IEEE Int. Conf. on Computer Vision 2: 734–741

    Article  Google Scholar 

  34. Wu, B., Ai, H., Huang, C.: LUT-based Adaboost for gender classification. Fourth Int. Conf. on Audio and Video-based Biometric Person Authentication, June 10–11, Guildford, U.K. (2003)

  35. Wu, B., Ai, H., Huang, C., Lao, S.: Fast rotation invariant multi-view face detection based on real Adaboost. Sixth Int. Conf. on Face and Gesture Recognition, pp. 79–84 (2004)

  36. Wu, J., Mullin, M.D, Rehg, J.M.: Linear asymmetric classifier for cascade detectors. Proceedings of the 22nd International Conference (ICML 2005), pp. 988–995 (2005)

  37. Wu J, Rehg J.M. and Mullin M.D. (2003). Learning a rare event detection cascade by direct feature selection. Proc. Advances in Neural Information Processing Systems 16 (NIPS*2003). MIT Press, Cambridge

    Google Scholar 

  38. Xiao R., Zhu L. and Zhang H.-J. (2003). Boosting chain learning for object detection. Nineth IEEE Int. Conf. on Computer Vision 1: 709–715

    Article  Google Scholar 

  39. Yang, M., Ahuja, N., Kriegman, D.: Mixtures of linear subspaces for face detection. Fourth IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 70–76 (2000)

  40. Yang, M.-H., Roth, D., Ahuja, N.: A SNoW-based face detector. Solla, S.A., Leen, T.K., Müller, K.-R. (eds.) Advances in Neural Information Processing Systems 12, pp. 855–861 (2000)

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Universidad de Chile, Av. Tupper 2007, 837-0451, Santiago, Chile

    Rodrigo Verschae, Javier Ruiz-del-Solar & Mauricio Correa

Authors
  1. Rodrigo Verschae
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Javier Ruiz-del-Solar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mauricio Correa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rodrigo Verschae.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Verschae, R., Ruiz-del-Solar, J. & Correa, M. A unified learning framework for object detection and classification using nested cascades of boosted classifiers. Machine Vision and Applications 19, 85–103 (2008). https://doi.org/10.1007/s00138-007-0084-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-007-0084-0

Keywords

Search

Navigation