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An Effective Feature Extraction Approach for Iris Recognition System

Affiliations

  • Department of Electronics and Communication Engineering, UIET, Panjab University,Chandigarh −160014, Panjab, India

Abstract


Background/Objectives: Iris recognition system refers to a system used for identifying different iris texture patterns for different applications. The research is aimed at developing a system that improves the efficiency of the iris recognition system and make it more reliable and robust. Methods: To this end, we have developed a system based on compound local binary pattern technique. Compound local binary technique is a spatial domain technique, which assign a 2P bit code to central pixel based on the local neighbourhood comprising of P neighbours. The operator takes into consideration both the sign and magnitude information of the central and corresponding neighbour grey values. The unique and abundant features extracted through Compound Local Binary Pattern (CLBP) operator act as input to the neural network classifier. Findings: The system has been tested over 50 eye images taken from CASIA database. Iris recognition system based on Compound local binary pattern technique along with neural network used as classifier improves the accuracy of system in comparison to existing feature extraction approaches. In this proposed research, recognition rate achieved is 96%.

Keywords

Compound Local Binary Pattern (CLBP), Feature Extraction, Iris Localisation, Iris Recognition, Neural Network.

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References


  • Bowyer KW, Hollingsworth K, Flynn PJ. Image Understanding for Iris Biometric: A Survey, Computer Vision and Image Understanding. 2007; 110(2):281−307.
  • Daughman J. How Iris Recognition Works, IEEE Transaction on Circuit and System for Video Technology.2004; 14:21−30.
  • Rakesh T, Khogare MG. Survey of Biometric Recognition System for Iris, International Journal of Emerging Technology and Advanced Engineering. 2012; 2(6).
  • Tallapragada VVS, Rajan EG. Iris Recognition Based on Combined Feature of GLCM and Wavelet Transform, First International Conference on Integrated Intelligent Computing, 2010, p.205−10.
  • Aydi W, Fadhel N, Masmoudi N, Kamoun L. A Robust Feature Extraction Method Based on Monogenic Filter for Iris Recognition System. IEEE. 2014.
  • Sharma A, Gupta R. Iris Recognition Based Learning Vector Quantization and Local Binary Pattern on Iris Matching, International Journal of Technical Research and Application. 2015; 3(5):7−14.
  • Hamouchene I. A New Texture Analysis Approach for Iris Recognition, AASRI Conference on Circuit and Signal Processing. 2014; 9:2−7.
  • Sarode NS, Patil AM. Iris Recognition using LBP and Classifier – Knn and NB, International Journal of Science and Research. 2015; 4(1).
  • Patwardhan T, Byalal RM. Implementation of Fusing Based Compound Local Binary Pattern Algorithm for Face Recognition, International Journal of Advanced Research in Computer Engineering and Technology. 2016 May; 5(5).
  • Ahmed F, Bari H, Hossain E. Person Independent Facial Expression Recognition Based on Compound Local Binary Pattern, International Arab Journal of Information and Technology. 2014 Mar; 11(2).
  • Hajari K. Improving Iris Recognition Performance using Local Binary Pattern and Combined RBFNN, International Journal of Engineering and Technology. 2015 Apr; 4(4).
  • Saminathan K, Chakravarthy T, Devi C. Comparative Study on Biometric Recognition Based on Hamming Distance and Multi Block Local Binary Pattern, Indian Journal of Science and Technology. 2015 Jun; 8(11).
  • Rai H, Yadav A. Iris Recognition using Combined Support Vector Machine and Hamming Distance, Expert Systems with Applications. 2014.

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