Abstract
In this chapter a new method for response integration, based on Choquet Integral is presented. A type-1 fuzzy system for edge detections based in Sobel and Morphological gradient is used, which is a pre-processing applied to the training data for better performance in the modular neural network. The Choquet integral is used how method to integrate the outputs of the modules of the modular neural networks (MNN). A database of faces was used to perform the pre-processing, the training, and the combination of information sources of the MNN.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhou, L.-G., Chen b, H.-Y., Merigó, J.M., Anna, M.: Uncertain generalized aggregation operators. Expert Syst. Appl. 39, 1105–1117 (2012)
Hidalgo, D., Castillo, O., Melin, P.: Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms. Inf. Sci. 179(13), 2123–2145 (2009)
Sánchez, D., Melin, P.: Modular neural network with fuzzy integration and its optimization using genetic algorithms for human recognition based on iris, ear and voice biometrics. In: Soft Computing for Recognition Based on Biometrics, pp. 85–102 (2010)
Sánchez, D., Melin, P., Castillo, O., Valdez, F.: Modular neural networks optimization with hierarchical genetic algorithms with fuzzy response integration for pattern recognition. MICAI 2, 247–258 (2012)
Melin, P., Gonzalez C., Bravo, D., Gonzalez F., Martínez G.: Modular neural networks and fuzzy Sugeno integral for pattern recognition: the case of human face and fingerprint. In: Hybrid Intelligent Systems: Design and Analysis. Springer, Heidelberg, Germany (2007)
Melin, P., Mendoza, O., Castillo O.: Face recognition with an improved interval Type-2 fuzzy logic Sugeno integral and modular neural networks. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41(5) (2011)
Sánchez, D., Melin, P.: Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure. Eng. Appl. Artif. Intell. 27, 41–56 (2014)
Wang, P., Xu, L., Zhou, S.M., Fan, Z., Li, Y., Feng, S.: A novel Bayesian learning method for information aggregation in modular neural networks. Expert Syst. Appl. 37, 1071–1074 (2010) (Elsevier)
Kwak, K.-C., Pedrycz, W.: Face recognition: a study in information fusion using fuzzy integral. Pattern Recogn. Lett. 26, 719–733 (2005)
Timonin, M.: Robust optimization of the Choquet integral. Fuzzy Sets Syst. 213, 27–46 (2013)
Yang, W., Chen, Z.: New aggregation operators based on the Choquet integral and 2-tuple linguistic information. Expert Syst. Appl. 39, 2662–2668 (2012)
Meena, Y.K., Arya, K.V., Kala, R.: Classification using redundant mapping in modular neural networks. In: Second World Congress on Nature and Biologically Inspired Computing, Kitakyushu, Fukuoka, Japan, 15–17 Dec 2010
Sugeno, M.: Theory of fuzzy integrals and its applications. Thesis Doctoral, Tokyo Institute of Technology, Tokyo, Japan (1974)
Murofushi, T., Sugeno, M.: Fuzzy measures and fuzzy integrals. Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan (2000)
Song, J., Li, J.: Lebesgue theorems in non-additive measure theory. Fuzzy Sets Syst. 149, 543–548 (2005)
Wang, Z., Klir, G.: Generalized Measure Theory. Springer, New York (2009)
Database ORL Face. Cambridge University Computer Laboratory. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html. Nov 2012
Mendoza, O., Melin, P., Castillo, O., Castro, J.: Comparison of fuzzy edge detectors based on the image recognition rate as performance index calculated with neural networks. In: Soft Computing for Recognition Based on Biometrics. Studies in Computational Intelligence, vol. 312, pp. 389–399 (2010)
Mendoza, O., Melin, P., Licea, G.: A hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral. Inf. Sci. Int. J. 179(13), 2078–2101 (2009)
Mendoza, O., Melin, P.: Quantitative evaluation of fuzzy edge detectors applied to neural networks or image recognition. Advances in Research and Developments in Digital Systems (2011)
Sánchez, D., Melin, P.: Multi-objective hierarchical genetic algorithm for modular granular neural network optimization. In: Soft Computing Applications in Optimization, Control, and Recognition, pp. 157–185 (2013)
Sánchez, D., Melin, P., Castillo, O., Valdez, F.: Modular granular neural networks optimization with Multi-Objective Hierarchical Genetic Algorithm for human recognition based on iris biometric. In: IEEE Congress on Evolutionary Computation, pp. 772–778 (2013)
Acknowledgments
We thank the MyDCI program of the Division of Graduate Studies and Research, UABC, Tijuana Institute of Technology, and the financial support provided by our sponsor CONACYT contract grant number: 189350.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Martínez, G.E., Melin, P., Mendoza, O.D., Castillo, O. (2014). Face Recognition with Choquet Integral in Modular Neural Networks. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-05170-3_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05169-7
Online ISBN: 978-3-319-05170-3
eBook Packages: EngineeringEngineering (R0)