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Generalizations of Aggregation Functions for Face Recognition

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Abstract

The problem of aggregation of the classification results is one of the most important task in image recognition or decision-making theory. There are many approaches to solve this problem as well as many operators and algorithms proposed such as voting, scoring, averages, and more advanced ones. In this paper, we examine the well-known existing and recently introduced to the practice of classification tasks generalizations of aggregation functions. Moreover, we introduce a few operators which have not been presented in the literature of the subject. The findings of this paper can shed a new light onto the theory of face recognition and help build advanced ensembles of classifiers.

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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). https://doi.org/10.1007/978-3-540-24670-1_36

    Chapter  Google Scholar 

  2. Al-Hmouz, R., Pedrycz, W., Daqrouq, K., Morfeq, A.: Development of multimodal biometric systems with three-way and fuzzy set-based decision mechanisms. Int. J. Fuzzy Syst. 20, 128–140 (2018)

    Article  Google Scholar 

  3. Alsina, C., Frank, M.J., Schweizer, B.: Associative Functions. Triangular Norms and Copulas. World Scientific, New Jersey (2006)

    Book  Google Scholar 

  4. Anderson, D.T., Scott, G.J., Islam, M.A., Murray, B., Marcum, R.: Fuzzy choquet integration of deep convolutional neural networks for remote sensing. In: Pedrycz, W., Chen, S.-M. (eds.) Computational Intelligence for Pattern Recognition. SCI, vol. 777, pp. 1–28. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-89629-8_1

    Chapter  Google Scholar 

  5. AT&T Laboratories Cambridge. [online] The Database of Faces. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html. Accessed 11 July 2018

  6. Baczyński, M., Bustince, H., Mesiar, R.: Aggregation functions: theory and applications. Fuzzy Set. Syst. 324, 325 (2017)

    Article  MathSciNet  Google Scholar 

  7. 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 

  8. Beliakov, G., Pradera, A., Calvo, T.: Aggregation Functions: A Guide for Practitioners. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73721-6

    Book  MATH  Google Scholar 

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

    Article  Google Scholar 

  10. Bustince, H., et al.: Pre-aggregation functions: definition, properties and construction methods. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 294–300 (2016)

    Google Scholar 

  11. Calvo, T., Mayor, G., Mesiar, R.: Aggregation Operators. New Trends and Applications. Physica-Verlag, Heidelberg (2014)

    MATH  Google Scholar 

  12. Chan, C.-H., Kittler, J., Messer, K.: Multi-scale local binary pattern histograms for face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 809–818. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74549-5_85

    Chapter  Google Scholar 

  13. Chan, C.H., Yan, F., Kittler, J., Mikolajczyk, K.: Full ranking as local descriptor for visual recognition: a comparison of distance metrics on \(s_n\). Pattern Recognit. 48, 1328–1336 (2015)

    Article  Google Scholar 

  14. Dolecki, M., Karczmarek, P., Kiersztyn, A., Pedrycz, W.: Utility functions as aggregation functions in face recognition. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, pp. 1–6 (2016)

    Google Scholar 

  15. Dimuro, G.P., Lucca, G., Sanz, J.A., Bustince, H., Bedregal, B.: CMin-Integral: a choquet-like aggregation function based on the minimum t-norm for applications to fuzzy rule-based classification systems. In: Torra, V., Mesiar, R., De Baets, B. (eds.) AGOP 2017. AISC, vol. 581, pp. 83–95. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-59306-7_9

    Chapter  Google Scholar 

  16. Haddadnia, J., Ahmadi, M.: N-feature neural network human face recognition. Image Vis. Comput. 22, 1071–1082 (2004)

    Article  Google Scholar 

  17. Hu, X., Pedrycz, W., Wang, X.: Comparative analysis of logic operators: a perspective of statistical testing and granular computing. Int. J. Approx. Reason. 66, 73–90 (2015)

    Article  MathSciNet  Google Scholar 

  18. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Technical report 07–49 (2007)

    Google Scholar 

  19. Karczmarek, P.: Selected problems of face recognition and decision-making theory. Lublin University of Technology Press, Lublin (2018)

    Google Scholar 

  20. Karczmarek, P., Kiersztyn, A., Pedrycz, W.: An evaluation of fuzzy measure for face recognition. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10245, pp. 668–676. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59063-9_60

    Chapter  Google Scholar 

  21. Karczmarek, P., Kiersztyn, A., Pedrycz, W.: Generalized Choquet integral for face recognition. Int. J. Fuzzy Syst. 20, 1047–1055 (2018)

    Article  Google Scholar 

  22. Karczmarek P., Kiersztyn, A., Pedrycz W.: On developing Sugeno fuzzy measure densities in problems of face recognition. Int. J. Mach. Intell. Sens. Sig. Process. 2, 80–96 (2017)

    Article  Google Scholar 

  23. Karczmarek, P., Pedrycz, W., Kiersztyn, A., Dolecki, M.: A comprehensive experimental comparison of the aggregation techniques for face recognition. Irani. J. Fuzzy Syst. (in press)

    Google Scholar 

  24. 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 

  25. 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 

  26. Kasiński, A., Florek, A., Schmidt, A.: The PUT face database. Image Process. Commun. 13, 59–64 (2008)

    Google Scholar 

  27. 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 (LNAI), vol. 8468, pp. 155–166. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07176-3_14

    Chapter  Google Scholar 

  28. Kwak, K.-C., Pedrycz, W.: Face recognition using fuzzy integral and wavelet decomposition method. IEEE Trans. Syst. Man. Cybern. B Cybern. 34, 1666–1675 (2004)

    Article  Google Scholar 

  29. 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 

  30. Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 828–837. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74549-5_87

    Chapter  Google Scholar 

  31. Lucca, G., de Vargas, R.R., Dimuro, G.P., Sanz, J.A., Bustince, H., Bedregal, B.R.C.: Analysing some t-norm-based generalizations of the Choquet integral for different fuzzy measures with an application to fuzzy rule-based classification systems. In: ENIAC 2014, Encontro Nac. Intelig. Artificial e Computacional. SBC, São Carlos, pp. 508–513 (2014)

    Google Scholar 

  32. Lucca, G., et al.: CC-integrals: Choquet-like copula-based aggregation functions and its application in fuzzy rule-based classification systems. Knowl.-Based Syst. 119, 32–43 (2017)

    Article  Google Scholar 

  33. Lucca, G., Sanz, J.A., Dimuro, G.P., Bedregal, B., Bustince, H.: Pre-aggregation functions constructed by CO-integrals applied in classification problems. In: Proceedings of IV CBSF, pp. 1–11 (2016)

    Google Scholar 

  34. Lucca, G., Sanz, J.A., Dimuro, G.P., Bedregal, B., Bustince, H., Mesiar, R.: CF-integrals: a new family of pre-aggregation functions with application to fuzzy rule-based classification systems. Inf. Sci. 435, 94–110 (2018)

    Article  Google Scholar 

  35. Lucca, G., et al.: The notion of pre-aggregation function. In: Torra, V., Narukawa, Y. (eds.) MDAI 2015. LNCS (LNAI), vol. 9321, pp. 33–41. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23240-9_3

    Chapter  Google Scholar 

  36. Lucca, G., et al.: Preaggregation functions: construction and an application. IEEE Trans. Fuzzy Syst. 24, 260–272 (2016)

    Article  Google Scholar 

  37. Melin, P., Felix, C., Castillo, O.: Face recognition using modular neural networks and the fuzzy Sugeno integral for response integration. Int. J. Intell. Syst. 20, 275–291 (2005)

    Article  Google Scholar 

  38. Milborrow, S., Morkel, J., Nicolls, F.: The MUCT landmarked face database. In: Pattern Recognition Association, South Africa (2010)

    Google Scholar 

  39. Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: Proceedings of 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1994, pp. 84–91 (1994)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  42. Yager, R.R., Kacprzyk, J.: The oRdered Weighted Averaging Operators: Theory and Applications. Springer, New York (2012)

    Google Scholar 

  43. Yale Face Database. [online]. http://vision.ucsd.edu/content/yale-face-database. Accessed 6 Apr 2017

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Acknowledgements

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 Computer Science, Lublin University of Technology, ul. Nadbystrzycka 36B, 20-618, 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
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  2. Adam Kiersztyn
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  3. Witold Pedrycz
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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

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

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

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Karczmarek, P., Kiersztyn, A., Pedrycz, W. (2019). Generalizations of Aggregation Functions for Face Recognition. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_17

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  • DOI: https://doi.org/10.1007/978-3-030-20915-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20914-8

  • Online ISBN: 978-3-030-20915-5

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