An Effective Block Weightage Based Technique for Iris Recognition Using Empirical Mode Decomposition

with the growing demands in security systems, iris recognition continues to be a significant solution for biometrics-based identification systems. There are several techniques for Iris Recognition such as Phase Based Technique, Non Filter-based Technique, Based on Wavelet Transform, Based on Empirical Mode Decomposition and many more. In this paper, we have developed a block weightage based iris recognition technique using Empirical Mode Decomposition (EMD) taking into consideration the drawbacks of the baseline technique. EMD is an adaptive multiresolution decomposition technique that is used for extracting the features from each block of the iris image. For matching the features of iris images with the test image, we make use of block weightage method that is designed in accordance with the irrelevant pixels contained in the blocks. For experimental evaluation, we have used the CASIA iris image database and the results clearly demonstrated that applying EMD in each block of normalized iris images makes it possible to achieve better accuracy in iris recognition than the baseline technique.

[1]  Okhwan Byeon,et al.  Efficient Iris Recognition through Improvement of Feature Vector and Classifier , 2001 .

[2]  Deva Durai Iris Recognition Using Modified Hierarchical Phase-Based Matching (HPM) Technique , 2010 .

[3]  Debasis Samanta,et al.  A Novel Approach to Iris Localization for Iris Biometric Processing , 2007 .

[4]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[5]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[6]  Lionel Torres,et al.  Person Identification Technique Using Human Iris Recognition , 2002 .

[7]  David Zhang,et al.  Automated Biometrics: Technologies and Systems , 2000 .

[8]  Koichi Ito,et al.  An Iris Recognition System Using Phase-Based Image Matching , 2006, 2006 International Conference on Image Processing.

[9]  Te-Ming Tu,et al.  Recognizing Human Iris by Modified Empirical Mode Decomposition , 2007, PSIVT.

[10]  Tai-hoon Kim,et al.  IRIS Texture Analysis and Feature Extraction for Biometric Pattern Recognition , 2008 .

[11]  Tieniu Tan,et al.  Biometric personal identification based on iris patterns , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[12]  Pengfei Shi,et al.  Iris Feature Extraction Using 2D Phase Congruency , 2005, Third International Conference on Information Technology and Applications (ICITA'05).

[13]  Debasis Samanta,et al.  Improved Feature Processing for Iris Biometric Authentication System , 2010 .

[14]  Dean G. Duffy,et al.  The Application of Hilbert-Huang Transforms to Meteorological Datasets , 2004 .

[15]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[16]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Danilo P. Mandic,et al.  Empirical Mode Decomposition for Trivariate Signals , 2010, IEEE Transactions on Signal Processing.

[18]  Te-Ming Tu,et al.  An Empirical Mode Decomposition Approach for Iris Recognition , 2006, 2006 International Conference on Image Processing.

[19]  Manesh Kokare,et al.  Iris Recognition Without Iris Normalization , 2010 .

[20]  Sandipan Pralhad Narote,et al.  Iris Based Recognition System Using Wavelet Transform , 2009 .

[21]  S. Noh,et al.  MULTIRESOLUTION INDEPENDENT COMPONENT ANALYSIS FOR IRIS IDENTIFICATION , 2002 .

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

[23]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[24]  Te-Ming Tu,et al.  Iris recognition with an improved empirical mode decomposition method , 2009 .

[25]  Sharath Pankanti,et al.  Biometrics: Personal Identification in Networked Society , 2013 .

[26]  John Daugman,et al.  The importance of being random: statistical principles of iris recognition , 2003, Pattern Recognit..

[27]  Chin-Wang Tao,et al.  A new matching approach for local feature based iris recognition systems , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[28]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[29]  Hiroshi Nakajima,et al.  An Effective Approach for Iris Recognition Using Phase-Based Image Matching , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Arivazhagan Selvaraj,et al.  Iris recognition using multi-resolution transforms , 2009, Int. J. Biom..

[31]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[32]  Norden E. Huang,et al.  A review on Hilbert‐Huang transform: Method and its applications to geophysical studies , 2008 .

[33]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  John Daugman,et al.  Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns , 2001, International Journal of Computer Vision.