Skip to main content
SpringerLink
Log in

An Evaluation of Fuzzy Measure for Face Recognition

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10245))

Included in the following conference series:

Abstract

In this paper, we analyze the properties and performance of the Choquet integral and fuzzy measure, particularly \(\lambda \)–fuzzy measure in the context of an aggregation of classifiers based on various facial areas. The fuzzy measure and Choquet integral have been shown to be an efficient aggregation techniques. However, in practice reported so far, the choice of the initial values of the measure corresponding to the saliency of facial features has been dependent upon the expert decision. Here, we propose an algorithmic way of finding these values. For this purpose a Particle Swarm Optimization (PSO) method is considered. The reported experimental results show that the method is more effective than the expert – centered approach.

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

Access this chapter

Log in via an institution
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 EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24670-1_36

    Chapter  Google Scholar 

  2. AT&T Laboratories Cambridge: AT&T Database of Faces. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

  3. Bansal, J.C., Singh, P.K., Saraswat, M., Verma, A., Jadon, S.S., Abraham, A.: Inertia weight strategies in particle swarm optimization. In: 2011 Third World Congress on Nature and Biologically Inspired Computing. Salamanca, pp. 633–640 (2011)

    Google Scholar 

  4. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997)

    Article  Google Scholar 

  5. Bereta, M., Pedrycz, W., Reformat, M.: Local descriptors and similarity measures for frontal face recognition: a comparative analysis. J. Vis. Commun. Image Represent. 24, 1213–1231 (2013)

    Article  Google Scholar 

  6. Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1042–1052 (1993)

    Article  Google Scholar 

  7. Davies, G., Ellis, H., Shepherd, J.: Cue saliency in faces as assessed by the Photofit technique. Perception 6, 263–269 (1977)

    Article  Google Scholar 

  8. Eberhart, R.C., Shi, Y.: Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the 2001 IEEE Congress on Evolutionary Computation, vol. 1, pp. 94–100 (2001)

    Google Scholar 

  9. Ekenel, H.K., Stiefelhagen, R.: Generic versus salient region-based partitioning for local appearance face recognition. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 367–375. Springer, Heidelberg (2009). doi:10.1007/978-3-642-01793-3_38

    Chapter  Google Scholar 

  10. Haig, N.D.: Exploring recognition with interchanged facial features. Perception 15, 235–247 (1986)

    Article  Google Scholar 

  11. Huang, G.B., Lee, H., Learned-Miller, E.: Learning hierarchical representations for face verification with convolutional deep belief networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2518–2525 (2012)

    Google Scholar 

  12. Johnston, R.A., Edmonds, A.J.: Familiar and unfamiliar face recognition: a review. Mem. 17, 577–596 (2009)

    Article  Google Scholar 

  13. Kanade, T.: Computer Recognition of Human Faces. Birkhauser Verlag, Basel (1977)

    Book  Google Scholar 

  14. Karczmarek, P., Kiersztyn, A., Pedrycz, W., Dolecki, M., Linguistic descriptors in face recognition. Appl. Soft Comput. (in press)

    Google Scholar 

  15. Karczmarek, P., Pedrycz, W., Kiersztyn, A., Dolecki, M.: An application of chain code-based local descriptor and its extension to face recognition. Pattern Recognit. 65, 26–34 (2017)

    Article  Google Scholar 

  16. Karczmarek, P., Pedrycz, W., Kiersztyn, A., Rutka, P.: A study in facial features saliency in face recognition: an analytic hierarchy process approach. Soft Comput. (in press)

    Google Scholar 

  17. Karczmarek, P., Pedrycz, W., Reformat, M., Akhoundi, E.: A study in facial regions saliency: a fuzzy measure approach. Soft Comput. 18, 379–391 (2014)

    Article  Google Scholar 

  18. Kacprzyk, J., Pedrycz, W.: Springer Handbook of Computational Intelligence. Springer, Heidelberg (2015)

    Book  MATH  Google Scholar 

  19. Kennedy, J.F., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Academic Press, San Diego (2001)

    Google Scholar 

  20. Kurach, D., Rutkowska, D., Rakus-Andersson, E.: Face classification based on linguistic description of facial features. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8468, pp. 155–166. Springer, Cham (2014). doi:10.1007/978-3-319-07176-3_14

    Chapter  Google Scholar 

  21. Kwak, K.-C., Pedrycz, W.: Face recognition: a study in information fusion using fuzzy integral. Pattern Recognit. Lett. 26, 719–733 (2005)

    Article  Google Scholar 

  22. Matthews, M.L.: Discrimination of Identikit constructions of faces: evidence for a dual processing strategy. Percept. Psychophys. 23, 153–161 (1978)

    Article  Google Scholar 

  23. Pedrycz, W., Gomide, F.: An Introduction to Fuzzy Sets: Analysis and Design. The MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  24. Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York (1980)

    MATH  Google Scholar 

  25. Saaty, T.L., Vargas, L.G.: Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. Springer, New York (2012)

    Book  MATH  Google Scholar 

  26. Sadr, J., Jarudi, I., Sinha, P.: The role of eyebrows in face recognition. Perception 32, 285–293 (2003)

    Article  Google Scholar 

  27. Shepherd, J., Davies, G., Ellis, H.: Studies of cue saliency. In: Davies, G., Ellis, H.D., Shepherd, J.W. (eds.) Perceiving and Remembering Faces, pp. 105–131. Academic Press, New York (1981)

    Google Scholar 

  28. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, pp. 69–73 (2002)

    Google Scholar 

  29. Sugeno, M.: Theory of fuzzy integral and its applications. Dissertation, Tokyo Institute of Technology (1974)

    Google Scholar 

  30. Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10.000 classes. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1891–1898 (2014)

    Google Scholar 

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

    Article  Google Scholar 

  32. Vignolo, L.D., Milone, D.H., Scharcanski, J.: Feature selection for face recognition based on multi-objective evolutionary wrappers. Expert Syst. Appl. 40, 5077–5084 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)

    Article  Google Scholar 

  35. Yan, Y., Osadciw, L.A.: Intra-difference based segmentation and face identification. In: Jain, A.K., Ratha, N.K. (eds.) Biometric Technology for Human Identification, Proceedings of SPIE 5404, pp. 502–510 (2004)

    Google Scholar 

Download references

Acknowledgments

The authors are supported by National Science Centre, Poland (grant no. 2014/13/D/ST6/03244). Support from the Canada Research Chair (CRC) program and Natural Sciences and Engineering Research Council is gratefully acknowledged (W. Pedrycz).

Author information

Authors and Affiliations

  1. Institute of Mathematics and Computer Science, The John Paul II Catholic University of Lublin, Ul. Konstantynów 1H, 20-708, Lublin, Poland

    Paweł Karczmarek & Adam Kiersztyn

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6R 2V4, Canada

    Witold Pedrycz

  3. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Witold Pedrycz

  4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Witold Pedrycz

Authors
  1. Paweł Karczmarek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adam Kiersztyn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Witold Pedrycz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paweł Karczmarek .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Karczmarek, P., Kiersztyn, A., Pedrycz, W. (2017). An Evaluation of Fuzzy Measure for Face Recognition. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_60

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59063-9_60

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59062-2

  • Online ISBN: 978-3-319-59063-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation