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Features Subset Selection using Improved Teaching Learning based Optimisation (ITLBO) Algorithms for Iris Recognition


  • Depatment of CSE, Bharath University, Chennai – 600073, Tamil Nadu, India
  • Depatment of CSE, Dhanalakshmi engineering College, Chennai – 601301, Tamil Nadu, India


Objective: Iris recognition is one of the emerging areas as the demand for security in social and personal areas is increasing day by day. The most challenging step in the process of iris recognition is accurate iris localization. Methods/Statistical Analysis: In this paper, we propose an iris recognition method in light of Teaching Learning Based Optimisation (ITLBO) to choose the ideal components subset. The iris information, for the most part, contains a tremendous number of textural elements and a similarly modest number of tests per subject, which make the accurate iris pattern classification challenging. Findings: Feature selection scheme is used to identify the most important and irrelevant features from extracted features set of a relatively high dimension based on some selection criterions. It is not generally handy to gather an extensive number of tests because of some security issues. In this paper, we propose ITLBO to enhance the feature subset determination by consolidating important results from different component choice strategies. Application/Improvements: The principle target of ITLBO is to accomplish an adjust the Recognition Rate (RR), the False Acceptance Rate (FAR), the False Reject Rate (FRR) and they chose feature subset measure. The proposed method is computationally successful with the RR of 97.97 % on the CASIA iris datasets.


Biometrics, CASIA, Feature Subset, ITLBO, Log Gabor Filter

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  • Schuckers SAC, Schmid NA, Abhyankar A, Dorairaj V, Boyce CK, Hornak LA. On techniques for angle compensation in nonideal iris recognition. IEEE Trans. SMC-B. 2007; 37(5):1176–90.
  • Bastos C, Tsang R, George C. Iris segmentation and recognition using 2d log-Gabor filters. Intelligent Data Engineering and Automated Learning-IDEAL; 2012. p. 443–50.
  • Deb K. Multi-Objective Optimization using Evolutionary Algorithms. J. Wiley Ltd., West Sussex; 2004.
  • Yao Peng et al. Iris recognition algorithm using modified logGabor filters. Pattern Recognition, 2006. ICPR 2006. 18th International Conference on. IEEE. 2006; 4.
  • Rai H, Anamika Y. Iris recognition using combined support vector machine and Hamming distance approach. Expert systems with applications. 2014; 41(2):588–93.
  • Son B, Won H, Kee G, Lee Y. Discriminant iris feature and support vector machines for iris recognition. International Conference on Image Processing. 2004; 2:865–8.
  • Tan F, Fu X, Zhang Y, Bourgeois AG. Improving feature subset selection using a genetic algorithm for microarray gene expression data. IEEE congress on evolutionary computation; 2006. p. 2529–34.
  • Agrawal S, Sharma S, Silakari S. Teaching Learning based Optimization (TLBO) based improved iris recognition system. Progress in Systems Engineering. Springer International Publishing; 2015. p. 735–40.
  • Roy K, Bhattacharya P. Iris Recognition Based on Collarette Region and Asymmetrical Support Vector Machines.Kamel, M., Campilho, A. (eds.) ICIAR 2007; Springer, Heidelberg; LNCS. 2007; 4633:854–65. Crossref.
  • Raghavendra C, Kumaravel A, Sivasubramanian S. Personal Authentication system by iris biometric using Log Gabor filter technique. Indian Journal of Science and Technology.2017; 10(11).
  • He F, Liu Y, Zhu X, Huang C, Han Y, Dong H. Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters.EURASIP Journal on Advances in Signal Processing. 2014; 1:95. Crossref.
  • Bansal A, Agarwal R, Sharma RK. Statistical feature extraction based iris recognition system. Sādhanā. 2016; 41(5):507–18.
  • Nalla PR, Kumar A. Toward More Accurate Iris Recognition Using Cross-Spectral Matching. IEEE Transactions on Image Processing. 2017; 26(1):208–21. Crossref.PMid:27740482
  • Hofbauer H et al. Experimental analysis regarding the influence of iris segmentation on the recognition rate. IET Biometrics. 2016; 5(3):200–11. Crossref.
  • Saiyed U, Dhara BC, Chanda B. A Novel Cancelable Iris Recognition System Based on Feature Learning Techniques.Information Sciences; 2017.
  • Christian R, Uhl A, Wild P. Iris biometrics: from segmentation to template security. Springer Science & Business Media. 2012; 59.
  • Kittipong C et al. An empirical study of distance metrics for k-nearest neighbor algorithm. The 3rd International Conference on Industrial Application Engineering (ICIAE2015); 2015. Crossref.
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