Face Recognition with a Sobel Edge Detector and the Choquet Integral as Integration Method in a Modular Neural Networks

In this paper a method for response integration of Modular Neural Networks, based on Choquet Integral applied to face recognition is presented. Type-1 and Type-2 fuzzy systems for edge detections based on the Sobel, which is a pre-processing applied to the training data for better performance in the modular neural network. The Choquet integral is an aggregation operator that in this case is used as a method to integrate the outputs of the modules of the modular neural networks (MNN).

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Oscar Castillo,et al.  Face Recognition with Choquet Integral in Modular Neural Networks , 2014, Recent Advances on Hybrid Approaches for Designing Intelligent Systems.

[3]  Tadashi Horiuchi,et al.  A Study on Japanese Historical Character Recognition Using Modular Neural Networks , 2009, 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC).

[4]  Pan Wang,et al.  A novel Bayesian learning method for information aggregation in modular neural networks , 2010, Expert Syst. Appl..

[5]  Patricia Melin,et al.  Soft Computing for Recognition Based on Biometrics , 2010, Soft Computing for Recognition Based on Biometrics.

[6]  R A Kirsch,et al.  Computer determination of the constituent structure of biological images. , 1971, Computers and biomedical research, an international journal.

[7]  Oscar Castillo,et al.  Interval type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimisation with genetic algorithms , 2008, Int. J. Biom..

[8]  Mikhail Timonin,et al.  Robust optimization of the Choquet integral , 2013, Fuzzy Sets Syst..

[9]  Patricia Melin,et al.  Modular Neural Network with Fuzzy Integration and Its Optimization Using Genetic Algorithms for Human Recognition Based on Iris, Ear and Voice Biometrics , 2010, Soft Computing for Recognition Based on Biometrics.

[10]  Jun Li,et al.  Lebesgue theorems in non-additive measure theory , 2005, Fuzzy Sets Syst..

[11]  Patricia Melin,et al.  Modular Neural Networks and Fuzzy Sugeno Integral for Pattern Recognition: The Case of Human Face and Fingerprint , 2007, Hybrid Intelligent Systems.

[12]  G. Klir,et al.  Generalized Measure Theory , 2008 .

[13]  G. Choquet Theory of capacities , 1954 .

[14]  C. Wojcik Springer international publishing switzerland , 2016 .

[15]  Witold Pedrycz,et al.  Face recognition: A study in information fusion using fuzzy integral , 2005, Pattern Recognit. Lett..

[16]  K. V. Arya,et al.  Classification using redundant mapping in modular neural networks , 2010, 2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC).

[17]  Wei Yang,et al.  New aggregation operators based on the Choquet integral and 2-tuple linguistic information , 2012, Expert Syst. Appl..

[18]  M. Sugeno FUZZY MEASURES AND FUZZY INTEGRALS—A SURVEY , 1993 .

[19]  菅野 道夫,et al.  Theory of fuzzy integrals and its applications , 1975 .

[20]  Hsiang-Chuan Liu,et al.  Choquet integral with respect to sigma-fuzzy measure , 2009, 2009 International Conference on Mechatronics and Automation.

[21]  Irwin Edward Sobel,et al.  Camera Models and Machine Perception , 1970 .

[22]  Oscar Castillo,et al.  Comparison of Fuzzy Edge Detectors Based on the Image Recognition Rate as Performance Index Calculated with Neural Networks , 2010, Soft Computing for Recognition Based on Biometrics.

[23]  Oscar Castillo,et al.  Face Recognition With an Improved Interval Type-2 Fuzzy Logic Sugeno Integral and Modular Neural Networks , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[24]  Oscar Castillo,et al.  Modular Neural Networks Optimization with Hierarchical Genetic Algorithms with Fuzzy Response Integration for Pattern Recognition , 2012, MICAI.

[25]  Thomas Bck Optimization by means of genetic algorithms , 1989 .