Abstract
In this paper a comparison among conventional Artificial Neural Networks (ANN), Ensemble Neural Networks (ENN) and Modular Granular Neural Networks (MGNNs) is performed. This comparison is performed use 10-fold cross-validation using from 1 to 12 images for the training phase. Some parameters of neural networks are randomly established such as: the number of sub modules (for ensemble and modular granular neural networks), the number of neurons of two hidden layers for each sub module and learning algorithm. A benchmark database is used to observe the neural networks performances.
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
Similar content being viewed by others
References
Abiyev, R., Altunkaya, K.: Personal Iris recognition using neural network. Department of Computer Engineering, Near East University, Lefkosa, North Cyprus (2008)
Azamm, F.: Biologically inspired modular neural networks. Virginia Polytechnic Institute and State University, Blacksburg, Virginia, Ph.D. thesis (2000)
Bakshe, R.C., Patil, A.M.: Hand geometry as a biometric for human identification. Int. J. Sci. Res. 4(1), 2744–2748 (2015)
Bargiela, A., Pedrycz, W.: The roots of granular computing. In: IEEE International Conference on Granular Computing (GrC), pp. 806–809 (2006)
Ch’ng, S.I., Seng, K.P., Ang, L.: Modular dynamic RBF neural network for face recognition. In: 2012 IEEE Conference on Open Systems (ICOS), pp. 1–6 (2012)
Chen, S.H., Jakeman, A.J., Norton, J.P.: Artificial intelligence techniques: an introduction to their use for modelling environmental systems. Math. Comput. Simul. 78, 379–400 (2008)
Database of Human Iris. Institute of Automation of Chinese Academy of Sciences (CASIA). http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp
Dilek, S., Çakır, H., Aydın, M.: Applications of artificial intelligence techniques to combating cyber crimes: a review. Int. J. Artif. Intell. Appl. (IJAIA) 6(1), 21–39 (2015)
Khan, A., Bandopadhyaya, T., Sharma, S.: Classification of stocks using self organizing map. Int. J. Soft Comput. Appl. 4, 19–24 (2009)
Masek, L., Kovesi, P.: MATLAB Source Code for a Biometric Identification System Based on Iris Patterns. The School of Computer Science and Software Engineering, The University of Western Australia (2003)
Melin, P., Castillo, O.: Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems, 1st edn. Springer, Heidelberg (2005)
Mohamad, M., Mohd Saman, M.Y.: Comparison of diverse ensemble neural network for large data classification. Int. J. Adv. Soft Comput. 7(3), 67–83 (2015)
Moreno, B., Sanchez, A., Velez, J.F.: On the use of outer ear images for personal identification in security applications. In: IEEE 33rd Annual International Carnahan Conference on Security Technology, pp. 469–476 (1999)
Pannu, A.: Artificial intelligence and its application in different areas. Int. J. Eng. Innov. Technol. (IJEIT) 4(10), 79–84 (2015)
Patil, A.M., Patil, D.S., Patil, P.S.: Iris recognition using gray level co-occurrence matrix and Hausdorff dimension. Int. J. Comput. Appl. 133(8), 29–34 (2016)
Pintea, C.M., Matei, O., Ramadan, R.A., Pavone, M., Niazi, M., Azar, A.T.: A fuzzy approach of sensitivity for multiple colonies on ant colony optimization. In: Soft Computing Applications, SOFA 2016. Advances in Intelligent Systems and Computing, vol. 634. Springer (2016)
Pulido, M., Castillo, O., Melin, P.: Genetic optimization of ensemble neural networks for complex time series prediction of the Mexican exchange. Int. J. Innov. Comput. Inf. Control 9(10), 4151–4166 (2013)
Qian, Y., Zhang, H., Li, F., Hu, Q., Liang, J.: Set-based granular computing: a lattice model. Int. J. Approximate Reasoning 55, 834–852 (2014)
Ruano, M.G., Ruano, A.E.: On the use of artificial neural networks for biomedical applications. In: Soft Computing Applications. Advances in Intelligent Systems and Computing, vol. 195. Springer
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)
Sánchez, D., Melin, P., Castillo, O.: Optimization of modular granular neural networks using a firefly algorithm for human recognition. Eng. Appl. Artif. Intell. 64, 172–186 (2017)
Sánchez, D., Melin, P., Castillo, O.: A grey wolf optimizer for modular granular neural networks for human recognition, vol. 2017, pp. 4180510:1–4180510:26 (2017)
Sankhe, A., Pawask, A., Mohite, R., Zagade, S.: Biometric identification system: a finger geometry and palm print based approach. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 5(3), 1756–1763 (2016)
Solanki, K., Pittalia, P.: Review of face recognition techniques. Int. J. Comput. Appl. 133(12), 20–24 (2016)
Sombra, A., Valdez, F., Melin, P., Castillo, O.: A new gravitational search algorithm using fuzzy logic to parameter adaptation. In: IEEE Congress on Evolutionary Computation 2013, Cancun, Mexico, pp. 1068–1074 (2013)
Valdez, F., Melin, P., Castillo, O.: A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation. Expert Syst. Appl. 41(14), 6459–6466 (2014)
Valdez, F., Melin, P., Castillo, O.: Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making. In: FUZZ-IEEE 2009, pp. 2114–2119 (2009)
Yao, Y.Y.: On modeling data mining with granular computing. In: 25th International Computer Software and Applications Conference (COMPSAC), pp. 638–649 (2001)
Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in hu-man reasoning and fuzzy logic. Fuzzy Sets Syst. 90(2), 111–127 (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Sánchez, D., Melin, P., Castillo, O. (2021). Comparison of Neural Network Models Applied to Human Recognition. In: Balas, V., Jain, L., Balas, M., Shahbazova, S. (eds) Soft Computing Applications. SOFA 2018. Advances in Intelligent Systems and Computing, vol 1221. Springer, Cham. https://doi.org/10.1007/978-3-030-51992-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-51992-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51991-9
Online ISBN: 978-3-030-51992-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)