Skip to main content
SpringerLink
Log in

A probabilistic hit-and-miss transform for face localization

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Face localization is needed for any face processing procedure whose applications range from biometric identification to content-based image retrieval. It consists in giving the image coordinates of the face. In this paper we propose a probabilistic pattern matching procedure for face localization in greyscale images similar to the morphological hit-and-miss-transform (HMT), which we call probabilistic HMT (PHMT). This procedure is defined on the morphological multiscale fingerprints (MMF), which are image features extracted from the morphological erosion/dilation scale spaces. The face location is estimated as the maximum likelihood image window matching both erosive and dilative MMF models of the object. The MMF models are computed at a discrete set of scales. The MMF models may be built up from a small set of training face images and do not involve numerically sophisticated training algorithms. Training does not use non-face sample images. Therefore resampling is not needed for the construction of the MMF models. The experimental results on the NIST Mugshot Identification Database endorse our claims about the accuracy and robustness of the proposed procedure.

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

Fig. 1a, b
Fig. 2
Fig. 3a, b
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Fukunaga K (1990) Introduction to statistical pattern recognition. Academic Press, London

  2. Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New York

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

    Article  MATH  Google Scholar 

  4. Rowley HA, Baluja S, Kanade T (1998) Neural network-based face detection. IEEE Trans Pattern Anal Mach Intell 20(1):23–38

    Article  Google Scholar 

  5. Sung K-K, Poggio T (1998) Example-based learning for view-based human face detection. IEEE Trans Pattern Anal Mach Intell 20(1):39–51

    Article  Google Scholar 

  6. Lin S-H, Kung S-Y, Lin L-J (1997) Face recognition/detection by probabilistic decision-based neural network. IEEE Trans Neural Netw 8(1):114–132

    Article  MATH  Google Scholar 

  7. Juell P, Marsh R (1996) A hierarchical neural network for human face detection. Pattern Recognition 29(5):781–787

    Article  Google Scholar 

  8. Dai Y, Nakano Y (1998) Recognition of facial images with low resolution using a hopfield memory model. Pattern Recognition 31(2):159–167

    Article  MATH  Google Scholar 

  9. Féraud R, Bernier OJ, Viallet J-E, Collobert M (2001) A fast and accurate face detector based on neural networks. IEEE Trans Pattern Anal Mach Intell 23(1):42–53

    Article  Google Scholar 

  10. Liu C (2003) A bayesian discriminating features method for face detection. IEEE Trans Pattern Anal Mach Intell 25(6):725–740

    Article  Google Scholar 

  11. Viola P, Jones M (2004) Robust real-time object detection. Int J Comput Vision, in press

  12. Lee CH, Kim JS, Park KH (1996) Automatic human face location in a complex background using motion and color information. Pattern Recognition 29(11):1877–1889

    Article  Google Scholar 

  13. Yang G, Huang TS (1994) Human face detection in a complex background. Pattern Recognition 27(1):53–63

    Article  MATH  Google Scholar 

  14. Yoo T-W, Oh I-S (1999) A fast algorithm for tracking human faces based on chromatic histograms. Pattern Recognition Lett 20:967–978

    Article  Google Scholar 

  15. McKenna SJ, Gong S, Raja Y (1998) Modelling facial colour and identity with gaussian mixtures. Pattern Recognition 31(12):1883–1992

    Article  Google Scholar 

  16. Wang J, Tan T (2000) A new face detection method based on shape information. Pattern Recognition Lett 21:463–471

    Article  MATH  Google Scholar 

  17. Yokoo Y, Hagiwara M (1996) Human faces detection method using genetic algorithms. In: Proceedings of international conference on evolutionary computation, pp 113–118

  18. Leung TK, Burl MC, Perona P (1995) Finding faces in cluttered scenes using random labeled graph matching. In: Proceedings of 5th international conference on computer vision, pp 637–644

  19. Yow KC, Cipolla R (1995) Finding initial estimates of the human face location. In: Proceedings of 2nd Asian Conference on Computer Vision, vol 3, pp 514–518

  20. Wiskott L (1999) The role of topographical constraints in face recognition. Pattern Recognition Lett 20(1):89–96

    MATH  Google Scholar 

  21. Moghaddam B, Pentland A (1997) Probabilistic visual learning for object representation. IEEE Trans Pattern Anal Mach Intell 19(7):696–710

    Article  Google Scholar 

  22. Pentland A (1997) Machine understanding of human behavior. In: Maybury MT (ed) Intelligent multimedia information retrieval. MIT Press, Cambridge, MA, pp 175–188

  23. Su MC, Chu CH (2001) A modified version of the K-means algorithm with a distance based on cluster symmetry. IEEE Trans Pattern Anal Mach Intell 23(6):674–680

    Article  Google Scholar 

  24. Lades M, Vorbruggen J, Buhmann J, Malburg CVD, Wurtz R (1993) Distortion invariant object recognition in the dynamic link architecture. IEEE Trans Comput 42(3):300–311

    Article  Google Scholar 

  25. Kotropoulos C, Tefas A, Pitas I (2000) Frontal face authentication using morphological elastic graph matching. IEEE Trans Image Process 9(4):555–560

    Article  Google Scholar 

  26. Tefas A, Kotropoulos C, Pitas I (2001) Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Trans Pattern Anal Mach Intell 23(7):735–746

    Article  Google Scholar 

  27. Han C-C, Liao H-YM, Yu KC, Chen L-H (1998) Fast face detection via morphology-based pre-processing. In: Proceedings of the 9th international conference on image analysis and processing, pp 469–476

  28. Marques F, Villaplana V, Buxes A (1999) Human face segmentation and tracking using connected operators and partition projection. In: Proceedings of international conference on computer vision, pp 1320–1324

  29. Raducanu B, Graña M, Albizuri FX, d’Anjou A (2001) Face localization based on the morphological multiscale fingerprints. Pattern Recognition Lett 22(3–4):359–371

    Google Scholar 

  30. Tsapatsoulis N, Avrithis Y, Kollias S (2001) Facial image indexing in multimedia databases. Pattern Anal Appl 4 (2–3):93–107

    Google Scholar 

  31. Fan J, Yau DKY, Elmagarmid AK, Aref WG (2001) Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans Image Process 10(10):1454–1466

    Article  MATH  Google Scholar 

  32. Hsu RL, Abdel-Mottaleb M, Jain AK (2002) Face detection in color images. IEEE Pattern Anal Mach Intell 24(5):696–706

    Article  Google Scholar 

  33. Serra J (1982) Image analysis and mathematical morphology. Academic Press, London, UK

  34. Gonzalez R C, Woods RE (1992) Digital image processing. Addison-Wesley, New York

  35. Haralick RM, Shapiro LG (1992) Computer and robot vision. Addison-Wesley, New York

  36. Dougherty ER (1994) Optimal mean-absolute-error filtering of gray-scale signals by the morphological hit-and-miss transform. J Math Imaging Vision 4:255–271

    Google Scholar 

  37. Bloomberg DS, Vincent L (2000) Pattern matching using the blur hit-miss transform. J Electron Imaging 9(2):140–150

    Article  Google Scholar 

  38. Khosravi M, Schafer RW (1996) Template matching based on a grayscale hit-and-miss transform. IEEE Trans Image Process 5(6):1060–1066

    Article  Google Scholar 

  39. Raducanu B, Graña M (2000) Face localization based on the morphological multiscale fingerprints. In: Proceedings of the international conference on pattern recognition, ICPR2000, pp 929–932

  40. Jackway PT, Deriche M (1996) Scale-space properties of the multiscale morphological dilation-erosion. IEEE Trans Pattern Anal Mach Intell 18(1):38–51

    Article  Google Scholar 

  41. Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing images using the hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15(9):850–863

    Article  Google Scholar 

  42. Wiskott L, Fellous J-M, Kuiger N, Malsburg C vd (1997) Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell 19(7):775–779

    Article  Google Scholar 

  43. Wilson RC, Hancock ER (1997) Structural matching by discrete relaxation. IEEE Trans Pattern Anal Mach Intell 19(6):634–648

    Article  Google Scholar 

  44. Boomgaard R vd, Smeulders AWM, Schavemaker JGM (1994) Image segmentation in morphological scale-space. In: Proceedings of international symposium on mathematical morphology, pp 15–16

  45. Kohonen T (1995) Self organizing maps. Springer, Berlin Heidelberg New York

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

    Google Scholar 

Download references

Acknowledgements

The work has been partially supported by grant TIC2000-0739-C04-02 of the Ministerio de Ciencia y Tecnologia. B. Raducanu benefited from a predoctoral grant from the University of the Basque Country (UPV/EHU).

Author information

Authors and Affiliations

  1. Department of CCIA, Universidad del Pais Vasco, Spain

    B. Raducanu, M. Graña, F. X. Albizuri & A. d’Anjou

Authors
  1. B. Raducanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Graña
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F. X. Albizuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. d’Anjou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Graña.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Raducanu, B., Graña, M., Albizuri, F.X. et al. A probabilistic hit-and-miss transform for face localization. Pattern Anal Applic 7, 117–127 (2004). https://doi.org/10.1007/s10044-004-0207-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-004-0207-4

Keywords

Search

Navigation