An intelligent method for iris recognition using supervised machine learning techniques

Abstract In the new millennium, with chaotic situation that exist in the world, people are threaten with multifarious terrorist attacks. There have been several intelligent ways in order to recognize and diminish these assaults wisely. Biometric traits have proven to be one of the useful ways for tackling these problems. Among all these traits, the iris recognition systems are appropriate tools for the human identification not only the iris pattern is well-known features, but also it has numerous features such as compactness representation, uniqueness texture, and stability. In spite of the fact that there have been published many approaches in these areas, there are still abundant problems in these approaches like time consuming, and computational complexity. In order to solve these obstacles, we propose the extravagant iris recognition methods that are based on combination of two dimensional Gabor kernel (2-DGK), step filtering (SF) and polynomial filtering (PF) for feature extraction and hybrid radial basis function neural network (RBFNN) with genetic algorithm (GA) for matching task. To assess the performance of the proposed method, we use two benchmarks in our algorithm and implemented it on CASIA-Iris V3, UBIRIS. V1 and UCI machine learning repository datasets. The experimental results of the proposed method reveal that the method is efficient in the iris recognition.

[1]  Ludmila I. Kuncheva,et al.  Initializing of an RBF network by a genetic algorithm , 1997, Neurocomputing.

[2]  Iris Recognition System based on Canny and LoG Edge Detection Methods , 2015 .

[3]  Neda Ahmadi,et al.  Hybrid robust iris recognition approach using iris image pre-processing, two-dimensional gabor features and multi-layer perceptron neural network/PSO , 2017, IET Biom..

[4]  Alireza Bahadori,et al.  Prediction of reservoir brine properties using radial basis function (RBF) neural network , 2015 .

[5]  David Zhang,et al.  A universal texture segmentation and representation scheme based on ant colony optimization for iris image processing , 2009, Comput. Math. Appl..

[6]  Prabir Bhattacharya,et al.  Improving Features Subset Selection Using Genetic Algorithms for Iris Recognition , 2008, ANNPR.

[7]  Ching Y. Suen,et al.  Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs , 2011, Eng. Appl. Artif. Intell..

[8]  Hao Zhang,et al.  Quantitative analysis of pit defects in an automobile engine cylinder cavity using the radial basis function neural network–genetic algorithm model , 2017 .

[9]  Jin Liu,et al.  PSO-RBFNN based optimized PNN classifier model , 2012, Proceedings of 2012 2nd International Conference on Computer Science and Network Technology.

[10]  Anu George,et al.  Effective Iris Recognition System , 2016 .

[11]  Andrew E. Suyker,et al.  A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data , 2010 .

[12]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Anamika Yadav,et al.  Iris recognition using combined support vector machine and Hamming distance approach , 2014, Expert Syst. Appl..

[14]  Norbert Michael Mayer,et al.  A multiscale polynomial filter for adaptive smoothing , 2007, Digit. Signal Process..

[15]  Vikrant Bhateja,et al.  A Robust Polynomial Filtering Framework for Mammographic Image Enhancement From Biomedical Sensors , 2013, IEEE Sensors Journal.

[16]  Oscar Castillo,et al.  Genetic Optimization of Neural Networks for Person Recognition Based on the Iris , 2012 .

[17]  Yogesh Golhar,et al.  Neural Network Approach to Iris Recognition in Noisy Environment , 2016 .

[18]  Ritesh Vyas,et al.  Iris recognition using 2-D Gabor filter and XOR-SUM code , 2016, 2016 1st India International Conference on Information Processing (IICIP).

[19]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[20]  Raghunath S. Holambe,et al.  Partial iris feature extraction and recognition based on a new combined directional and rotated directional wavelet filter banks , 2012, Neurocomputing.

[21]  Michele Nappi,et al.  Multimodal authentication on smartphones: Combining iris and sensor recognition for a double check of user identity , 2016, Pattern Recognit. Lett..

[22]  Sanjit K. Mitra,et al.  Quadratic filters for image contrast enhancement , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

[23]  Jing Huang,et al.  Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet , 2010, Neurocomputing.

[24]  Santanu Chaudhury,et al.  Iris recognition based on sparse representation and k-nearest subspace with genetic algorithm , 2016, Pattern Recognit. Lett..

[25]  Feng Ran,et al.  A hardware implementation of a radial basis function neural network using stochastic logic , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[26]  Luís A. Alexandre,et al.  Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Sridha Sridharan,et al.  Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective , 2018, IEEE Access.

[28]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Stanley S. Ipson,et al.  A multi-biometric iris recognition system based on a deep learning approach , 2017, Pattern Analysis and Applications.

[30]  Jaihie Kim,et al.  Eye detection in a facial image under pose variation based on multi-scale iris shape feature , 2017, Image Vis. Comput..

[31]  Neda Ahmadi,et al.  Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier , 2018, Neural Computing and Applications.

[32]  Neda Ahmadi,et al.  Iris Texture Recognition based on Multilevel 2-D Haar Wavelet Decomposition and Hamming Distance Approach , 2018 .

[33]  Sudeep D. Thepade,et al.  Score Level Fusion Based Bimodal Biometric Identification Using Thepade's Sorted n-ary Block Truncation Coding with Variod Proportions of Iris and Palmprint Traits , 2016 .

[34]  Tieniu Tan,et al.  DeepIris: Learning pairwise filter bank for heterogeneous iris verification , 2016, Pattern Recognit. Lett..

[35]  Yusuf Hartono,et al.  Trip Attraction Model Using Radial Basis Function Neural Networks , 2015 .

[36]  Yogesh Golhar,et al.  Novel technique for removing corneal reflection in noisy environment — Enhancing iris recognition performance , 2016, 2016 International Conference on Signal and Information Processing (IConSIP).

[37]  Tieniu Tan,et al.  A Code-Level Approach to Heterogeneous Iris Recognition , 2017, IEEE Transactions on Information Forensics and Security.

[38]  Kang Ryoung Park,et al.  Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment , 2017, Symmetry.

[39]  Sunny Behal,et al.  Face Recognition System Using Genetic Algorithm , 2016 .

[40]  F. Carravetta,et al.  Polynomial filtering of discrete-time stochastic linear systems with multiplicative state noise , 1997, IEEE Trans. Autom. Control..

[41]  Bo Hu,et al.  A Polynomial Prediction Filter Method for Estimating Multisensor Dynamically Varying Biases , 2007 .

[42]  Bernadette Dorizzi,et al.  Effective elliptic fitting for iris normalization , 2013, Comput. Vis. Image Underst..

[43]  Mariusz Zubert,et al.  A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform , 2012, Pattern Recognit. Lett..

[44]  Ching-Han Chen,et al.  High performance iris recognition based on 1-D circular feature extraction and PSO-PNN classifier , 2009, Expert Syst. Appl..

[45]  M.B. Pereira,et al.  Application of Genetic Algorithms to Improve the Reliability of an Iris Recognition System , 2005, 2005 IEEE Workshop on Machine Learning for Signal Processing.

[46]  David Menotti,et al.  A Benchmark for Iris Location and a Deep Learning Detector Evaluation , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[47]  Amin Shokrollahi,et al.  Applying a robust solution based on expert systems and GA evolutionary algorithm for prognosticating residual gas saturation in water drive gas reservoirs , 2014 .

[48]  Zne-Jung Lee,et al.  Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment , 2008, Appl. Soft Comput..

[49]  Mohammed Bennamoun,et al.  A Novel Adaptive Kernel for the RBF Neural Networks , 2016, Circuits, Systems, and Signal Processing.

[50]  Julian Ashbourn.  Biometrics in the New World: The Cloud, Mobile Technology and Pervasive Identity , 2014 .

[51]  Arun Ross,et al.  Iris Image Reconstruction from Binary Templates , 2016 .

[52]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[53]  Amitava Chatterjee,et al.  A comparative study of adaptation algorithms for nonlinear system identification based on second order Volterra and bilinear polynomial filters , 2011 .