T. Ahonen, A. Hadid, M. Pietikainen, Face recognition with local binary patterns, In: Proceedings of the 8th
European Conference on Computer Vision, Lecture Notes in Computer Science, 3021 (2004), 469{481.
[2] R. Al-Hmouz, W. Pedrycz, K. Daqrouq, A. Morfeq, Development of multimodal biometric systems with three-way
and fuzzy set-based decision mechanisms, International Journal of Fuzzy Systems, 20(1) (2017), 128{140.
[3] F. R. Al-Osaimi, M. Bennamoun, A. Mian, Spatially optimized data-level fusion of texture and shape for face
recognition, IEEE Transactions on Image Processing, 21 (2012), 859{872.
[4] C. Alsina, M. J. Frank, B. Schweizer, Associative functions. Triangular norms and copulas, World Scienti fic, New
Jersey, 2006.
[5] AT&T Laboratories Cambridge. The Database of Faces. http://www.cl.cam.ac.uk/ research/
dtg/attarchive/facedatabase.html. Accessed 10 May 2016.
[6] A. I. Awad, A. E. Hassanien, Impact of some biometric modalities on forensic science, In: A. K. Muda, Y.-H. Choo,
A. Abraham, and S. N. Srihari (Eds.), Computational intelligence in digital forensics: Forensic investigation and
applications, Studies in Computational Intelligence, 555 (2014), 47-62.
[7] P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition using class speci fic linear
projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (1997), 711-720.
[8] G. Beliakov, A. Pradera, T. Calvo, Aggregation functions: A guide for practitioners, Springer-Verlag, Berlin Heidelberg,
2007.
[9] M. Bereta, P. Karczmarek, W. Pedrycz, M. Reformat, Local descriptors in application to the aging problem in face
recognition, Pattern Recognition, 46 (2013), 2634{2646.
[10] S. D. Bharkad, M. Kokare, Performance evaluation of distance metrics: Application to fi ngerprint recognition,
International Journal of Pattern Recognition and Arti ficial Intelligence, 25 (2011), 777-806.
[11] R. Brunelli, T. Poggio, Face recognition: Features versus templates, IEEE Transactions on Pattern Analysis and
Machine Intelligence, 15 (1993), 1042-1052.
[12] H. Bustince, J. Fernandez, R. Mesiar, T. Calvo, Aggregation functions in theory and in practice, Springer, Berlin,
2013.
[13] T. Calvo, G. Mayor, R. Mesiar, Aggregation operators. New trends and applications, Physica-Verlag, Heidelberg,
2014.
[14] C. Campomanes-Alvarez, O. Ibanez, O. Cordon, Experimental study of different aggregation functions for model-
ing craniofacial correspondence in craniofacial superimposition, In: 2016 IEEE International Conference on Fuzzy
Systems (FUZZ-IEEE), (2016), 437{444.
[15] C. H. Chan, M. A. Tahir, J. Kittler, M. Pietikainen, Multiscale local phase quantization for robust component-based
face recognition using kernel fusion of multiple descriptors, IEEE Transactions on Pattern Analysis and Machine
Intelligence, 35 (2013), 1164{1177.
[16] C. Chen, A. Dantcheva, A. Ross, An ensemble of patch-based subspaces for makeup-robust face recognition, Information
Fusion, 32 (2016), 80-92.
[17] J. H. Chiang, Aggregating membership values by a Choquet-fuzzy-integral based operator, Fuzzy Sets and Systems,
114 (2000), 367-375.
[18] J. A. Dargham, A. Chekima, M. Hamdan, Hybrid component-based face recognition system, In: S. Omatu et al.
(Eds.), Distributed computing and artifi cial intelligence, Advances in Intelligent and Soft Computing, 151 (2016),
573-580.
[19] S. Das, D. Guha, Power harmonic aggregation operator with trapezoidal intuitionistic fuzzy numbers for solving
MAGDM problems, Iranian Journal of Fuzzy Systems, 12(6) (2015), 41-74.
[20] M. Dolecki, P. Karczmarek, A. Kiersztyn, W. Pedrycz, Utility functions as aggregation functions in face recognition,
In: Proc. 2016 IEEE Symposium Series on Computational Intelligence (SSCI 2016), 2016.
[21] H. K. Ekenel, R. Stiefelhagen, Local appearance based face recognition using discrete cosine transform, In: Proceedings
of the 13th European Signal Processing Conference (EUSIPCO), Antalya, (2005), 2484-2488.
[22] H. K. Ekenel, R. Stiefelhagen, Generic versus salient region-based partitioning for local appearance face recognition,
In: M. Tistarelli and M. S. Nixon (Eds.), Advances in Biometrics, Lecture Notes in Computer Science, 5558 (2009),
367-375.
[23] R. Gottumukkal, V. K. Asari, An improved face recognition technique based on modular PCA approach, Pattern
Recognition Letters, 25 (2004), 429-436.
[24] M. Grabisch, Fuzzy integral in multicriteria decision making, Fuzzy Sets and Systems, 69 (1995), 279-298.
[25] M. Grabisch, J.-L. Marichal, R. Mesiar, E. Pap, Aggregation functions, Cambridge University Press, Cambridge,
2009.
[26] J. Haddadnia, M. Ahmadi, N-feature neural network human face recognition, Image and Vision Computing, 22
(2004), 1071-1082.
[27] B. Heisele, V. Blanz, Morphable models for training a component-based face recognition system, In: W. Zhao and
R. Chellappa (Eds.), Face processing: Advanced modeling and methods, 1055 (2006), 439{462.
[28] B. Heisele, P. Ho, J. Wu, T. Poggio, Face recognition: Component-based versus global approaches, Computer Vision
and Image Understanding, 91 (2003), 6-21.
[29] B. Heisele, T. Serre, T. Poggio, A component-based framework for face detection and identi fication, International
Journal of Computer Vision, 74 (2007), 167{181.
[30] G. B. Huang, M. Mattar, T. Berg, E. Learned-Miller, Labeled faces in the wild: A database for studying face
recognition in unconstrained environments, In: Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and
Recognition, ID: inria-00321923 (2007), 1{14.
[31] G. B. Huang, H. Lee, E. Learned-Miller, Learning hierarchical representations for face verifi cation with convo-
lutional deep belief networks, In: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on,
(2012) 2518{2525.
[32] Z.-H. Huang, W.-J. Li, J. Wang, T. Zhang, Face recognition based on pixel-level and feature-level fusion of the
top-levels wavelet sub-bands, Information Fusion, 22 (2015), 95-104.
[33] G. Jarillo, W. Pedrycz, M. Reformat, Aggregation of classi fiers based on image transformations in biometric face
recognition, Machine Vision and Applications, 19 (2008), 125-140.
[34] P. Karczmarek, A. Kiersztyn, W. Pedrycz, An evaluation of fuzzy measure for face recognition. In: L. Rutkowski,
M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. Zadeh, and J. Zurada J. (Eds.), ICAISC 2017, LNCS, 10245
(2017), 668{676.
[35] P. Karczmarek, A. Kiersztyn, W. Pedrycz, On developing Sugeno fuzzy measure densities in problems of face
recognition, International Journal of Machine Intelligence and Sensory Signal Processing, 2 (2017), 80-96.
[36] P. Karczmarek, A. Kiersztyn, W. Pedrycz, Generalized Choquet integral for face recognition, International Journal
of Fuzzy Systems, 20(3) (2017), 1047{1055.
[37] P. Karczmarek, A. Kiersztyn, W. Pedrycz, M. Dolecki, An application of chain code-based local descriptor and its
extension to face recognition, Pattern Recognition, 65 (2017), 26-34.
[38] P. Karczmarek, W. Pedrycz, M. Reformat, E. Akhoundi, A study in facial regions saliency: A fuzzy measure
approach, Soft Computing, 18 (2014), 379-391.
[39] T.-K. Kim, H. Kim, H., W. Hwang, J. Kittler, Component-based LDA face description for image retrieval and
MPEG-7 standardisation, Image and Vision Computing, 23 (2005), 631-642.
[40] J. Kittler, M. Hatef, R. P. W. Duin, J. Matas, On combining classi fiers, IEEE Transactions on Pattern Analysis
and Machine Intelligence, 20 (1998), 226{239.
[41] E. P. Klement, R. Mesiar, Logical, algebraic, analytic, and probabilistic aspects of triangular norms, Elsevier,
Amsterdam, 2005.
[42] E. P. Klement, R. Mesiar, E. Pap, Triangular norms. Kluwer Academic Publishers, Dordrecht, 2000.
[43] D. Kurach, D. Rutkowska, E. Rakus-Andersson, Face classi fication based on linguistic description of facial features.
In: L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. A. Zadeh, and J. M. Zurada (Eds.), ICAISC
2014, Part II, LNAI, 8468 (2014), 155-166.
[44] K.-C. Kwak, W. Pedrycz, Face recognition using fuzzy integral and wavelet decomposition method, IEEE Transactions
on Systems, Man, and Cybernetics, Part B: Cybernetics, 34 (2004), 1666-1675.
[45] K.-C. Kwak, W. Pedrycz, Face recognition: A study in information fusion using fuzzy integral, Pattern Recognition
Letters, 26 (2005), 719-733.
[46] Y. Lei, M. Bennamoun, A. A. El-Sallam, An efficient 3D face recognition approach based on the fusion of novel
local low-level features, Pattern Recognition, 46 (2013), 24-37.
[47] W. L. Liu, X. Q. Song, Q. Z. Zhang, S. B. Zhang, (T) fuzzy integral of multi-dimensional function with respect to
multi-valued measure, Iranian Journal of Fuzzy Systems, 9(3) (2012), 111{126.
[48] Z. Liu, C. Liu, Fusion of color, local spatial and global frequency information for face recognition, Pattern Recognition,
43 (2010), 2882-2890.
[49] G. Lucca, J. A., Sanz, G. P. Dimuro, B. Bedregal, R. Mesiar, A. Kolesarova, H. Bustince, Pre-aggregation functions:
Construction and an application, IEEE Transactions on Fuzzy Systems, 24 (2016), 260-272.
[50] G. E. Martnez, P. Melin, O. D. Mendoza, O. Castillo, Face recognition with Choquet integral in modular neural
networks. In: O. Castillo, P. Melin, W. Pedrycz, and J. Kacprzyk (Eds.), Recent advances on hybrid approaches for
designing intelligent systems. Studies in Computational Intelligence, 547 (2014), 437{449.
[51] G. E. Martnez, P. Melin, O. D. Mendoza, O. Castillo, Face recognition with a Sobel edge detector and the Choquet
integral as integration method in a modular neural networks. In: P. Melin, O. Castillo, and J. Kacprzyk (Eds.),
Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization. Studies in
Computational Intelligence, 601 (2015), 59{70.
[52] P. Melin, C. Felix, O. Castillo, Face recognition using modular neural networks and the fuzzy Sugeno integral for
response integration, International Journal of Intelligent Systems, 20 (2005), 275-291.
[53] A. R. Mirhosseini, H. Yan, K.-M. Lam, T. Pham, Human face image recognition: An evidence aggregation approach,
Computer Vision and Image Understanding, 71 (1998), 213-230.
[54] T. Murofushi, M. Sugeno, An interpretation of fuzzy measures and the Choquet integral as an integral with respect
to a fuzzy measure, Fuzzy Sets and Systems, 29 (1989), 201-227.
[55] C. Naveena, V. N. Manjunath Aradhya, S. K. Niranjan, The study of different similarity measure techniques in
recognition of handwritten characters. In: Proceedings of the International Conference on Advances in Computing,
Communications and Informatics (ICACCI-2012), ACM, (2012), 781-787.
[56] S.-K. Oh, S.-H. Yoo, W. Pedrycz, Design of face recognition algorithm using PCA -LDA combined for hybrid
data pre-processing and polynomial-based RBF neural networks : Design and its application, Expert Systems with
Applications, 40 (2013), 1451-1466.
[57] W. Pedrycz, F. Gomide, An introduction to fuzzy sets: Analysis and design. The MIT Press, Cambridge, 1998.
[58] A. Pentland, B. Moghaddam, T. Starner, View-based and modular eigenspaces for face recognition. In: Computer
Vision and Pattern Recognition. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on, (1994),
84{91.
[59] V. Perlibakas, Distance measures for PCA-based face recognition, Pattern Recognition Letters, 25 (1994), 711-724.
[60] P. J. Phillips, J. Wechsler, J. Huang, P. Rauss, The FERET database and evaluation procedure for face recognition
algorithms, Image and Vision Computing, 16 (1998), 295-306.
[61] P. V. W. Radtke, E. Granger, R. Sabourin, D. O. Gorodnichy, Skew-sensitive boolean combination for adaptive
ensembles An application to face recognition in video surveillance, Information Fusion, 20 (2014), 31-48.
[62] M. Sugeno, Theory of fuzzy integral and its applications, Dissertation. Tokyo Institute of Technology, Tokyo, 1974.
[63] Y. Sun, X. Wang, X. Tang, Hybrid deep learning for face veri fication, IEEE Transactions on Pattern Analysis and
Machine Intelligence, 38 (2016), 1997{2009.
[64] Y. Sun, X. Wang, X. Tang, Deep learning face representation from predicting 10,000 classes. In: The IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), (2014), 1891{1898.
[65] P. Tome, R. Vera-Rodriguez, J. Fierrez, Analysing facial regions for face recognition using forensic protocols. In:
J. M. Corchado et al. (Eds.), Highlights on practical applications of agents and multi-agent systems, 365 (2013),
223{230.
[66] V. Torra, Y. Narukawa, Modeling decisions. Information fusion and aggregation operators. Springer-Verlag, Berlin
Heidelberg, 2007.
[67] M. Turk, A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, 3 (1991), 71-86.
[68] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, Y. Ma, Robust face recognition via sparse representation, IEEE
Transactions on Pattern Analysis and Machine Intelligence, 31 (2009), 210{227.
[69] Y. Xue, C. S. Tong, W. Zhang, Survey of distance measures for NMF-based face recognition. In: Y. Wang, Y.
Cheung, and H. Liu (Eds.), Computational Intelligence and Security, CIS 2006, Lecture Notes in Computer Science,
4456 (2007), 1039{1049.
[70] R. R. Yager, J. Kacprzyk, The ordered weighted averaging operators: Theory and Applications. Springer Science+
Business Media, New York, 2012.
[71] D. Yan, Y. Shen, Y. Yan, H. Wang, Non-linear feature fusion based on polynomial correlation filter for face
recognition. In: Lecture Notes in Computer Science, 8261 (2013), 312{319.
[72] Y. Yan, L. A. Osadciw, Intradifference-based segmentation and face identi cation. In: A. K. Jain and N. K. Ratha
(Eds.), Biometric technology for human identi cation, Proceedings of SPIE, 5404 (2004), 502-510.
[73] W. Zhao, R. Chellappa, P. J. Phillips, A. Rosenfeld, Face recognition: A literature survey, ACM Computing
Surveys, 35 (2003), 399458.
[74] Q. Zhu, Y. Xu, Multi-directional two-dimensional PCA with matching score level fusion for face recognition, Neural
Computing and Applications, 23 (2013), 169-174.
)