A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition.

[1]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  P. Melin,et al.  Optimization of modular granular neural networks using a firefly algorithm for human recognition , 2017, Eng. Appl. Artif. Intell..

[3]  Lotfi A. Zadeh,et al.  Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems , 1998, Soft Comput..

[4]  Aboul Ella Hassanien,et al.  Grey wolf optimizer-based back-propagation neural network algorithm , 2016, 2016 12th International Computer Engineering Conference (ICENCO).

[5]  Daniel A. Ashlock,et al.  Evolutionary computation for modeling and optimization , 2005 .

[6]  L. Mech.  Alpha Status, Dominance, and Division of Labor in Wolf Packs , 1999 .

[7]  John Iovine,et al.  Understanding Neural Networks The Experimenter's Guide , 2012 .

[8]  Monika Jhapate,et al.  An Efficient Human Identification on the Biometric Gait Recognition System using the Inner Angle of the Triangle , 2016 .

[9]  D. Wiesmann,et al.  Evolutionary Optimization Algorithms in Computational Optics , 1999 .

[10]  Xin-She Yang,et al.  Firefly Algorithm: Recent Advances and Applications , 2013, ArXiv.

[11]  S. Sriraman,et al.  Non Linear Control of ZCS DC: DC Buck Converter , 2013 .

[12]  Arun Ross,et al.  Human recognition using biometrics: an overview , 2007, Ann. des Télécommunications.

[13]  Patricia Melin,et al.  Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation , 2016 .

[14]  Min Wang,et al.  A Scale Invariant Feature Transform Based Method , 2013, J. Inf. Hiding Multim. Signal Process..

[15]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[16]  Pravin S. Patil,et al.  Iris Recognition using Gray Level Co-occurrence Matrix and Hausdorff Dimension , 2016 .

[17]  L. Zadeh,et al.  Data mining, rough sets and granular computing , 2002 .

[18]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[19]  Patricia Melin,et al.  Modular neural network integrator for human recognition from ear images , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[20]  W. Pedrycz,et al.  Granular computing and intelligent systems : design with information granules of higher order and higher type , 2011 .

[21]  Rahul C. Bakshe,et al.  Hand Geometry as a Biometric for Human Identification , 2015 .

[22]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[23]  Bart L. M. Happel,et al.  Design and evolution of modular neural network architectures , 1994, Neural Networks.

[24]  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.

[25]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[26]  Mansoor Farooq,et al.  Genetic Algorithm Technique in Hybrid Intelligent Systems for Pattern Recognition , 2015 .

[27]  Saeid Sanei,et al.  A New Neural Network Approach for Face Recognition based on Conjugate Gradient Algorithms and Principal Component Analysis , 2013 .

[28]  Seyed Mohammad Mirjalili.  How effective is the Grey Wolf optimizer in training multi-layer perceptrons , 2014, Applied Intelligence.

[29]  Arun Ross,et al.  50 years of biometric research: Accomplishments, challenges, and opportunities , 2016, Pattern Recognit. Lett..

[30]  Xin-She Yang,et al.  Bat algorithm: literature review and applications , 2013, Int. J. Bio Inspired Comput..

[31]  Yiyu Yao,et al.  Perspectives of granular computing , 2005, 2005 IEEE International Conference on Granular Computing.

[32]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[33]  Patricia Melin,et al.  A firefly algorithm for modular granular neural networks optimization applied to iris recognition , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[34]  Patricia Melin,et al.  Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure , 2014, Eng. Appl. Artif. Intell..

[35]  Zong Woo Geem,et al.  Harmony Search and Nature-Inspired Algorithms for Engineering Optimization , 2013, J. Appl. Math..

[36]  Mohammad Reza Mosavi,et al.  Classification of sonar data set using neural network trained by Gray Wolf Optimization , 2016 .

[37]  Oscar Castillo,et al.  Genetic optimization of modular neural networks with fuzzy response integration for human recognition , 2012, Inf. Sci..

[38]  Libor Masek,et al.  MATLAB Source Code for a Biometric Identification System Based on Iris Patterns , 2003 .

[39]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[40]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[41]  Oscar Castillo,et al.  Optimization of modular granular neural networks using a hierarchical genetic algorithm based on the database complexity applied to human recognition , 2015, Inf. Sci..

[42]  L. Zadeh,et al.  Fuzzy Logic for the Management of Uncertainty , 1992 .

[43]  Kah Phooi Seng,et al.  Modular dynamic RBF neural network for face recognition , 2012, 2012 IEEE Conference on Open Systems.

[44]  N. Ghadimi,et al.  A hybrid neural network-gray wolf optimization algorithm for melanoma detection , 2017 .

[45]  Shyi-Ming Chen,et al.  Granular Computing and Intelligent Systems , 2011 .

[46]  Mohammad Shakeel Laghari,et al.  Offline Handwritten Signature Recognition , 2011 .

[47]  Deyi Li,et al.  Artificial Intelligence with Uncertainty , 2004, CIT.