AN EFFECTIVE BLOCK WEIGHTAGE BASED TECHNIQUE FOR IRIS RECOGNITION USING EMPIRICAL MODE DECOMPOSITION

Dr, Deepak Sharma
Dr, Deepak Sharma * §
Contact
§ Kurukshetra University

Send Message

To: Author

AN EFFECTIVE BLOCK WEIGHTAGE BASED TECHNIQUE FOR IRIS RECOGNITION USING EMPIRICAL MODE DECOMPOSITION

Article Fingerprint

ReserarchID

CSTFD7ZY

AN EFFECTIVE BLOCK WEIGHTAGE BASED TECHNIQUE FOR IRIS RECOGNITION USING EMPIRICAL MODE DECOMPOSITION Banner

AI TAKEAWAY

Connecting with the Eternal Ground
Access Review Comments
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu
Font Type
Font Size
Font Size
Bedground

Abstract

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.

References

45 Cites in Article
  1. Somnath Dey,Debasis Samanta (2007). A Novel Approach To Iris Localization For Iris Biometric Processing.
  2. Lenina Birgale,M Kokare (2010). Iris Recognition without Iris Normalization.
  3. J Daugman (1993). High confidence visual recognition of persons by a test of statistical independence.
  4. A Jain,R Bolle,S Pankanti (1999). Biometrics: Personal Identification in Networked Society.
  5. D Zhang (2000). Automated Biometrics: Technologies and Systems.
  6. Xiaoyan Yuan,Pengfei Shi (2005). Iris Feature Extraction Using 2D Phase Congruency.
  7. J Daugman (1993). High confidence visual recognition of persons by a test of statistical independence.
  8. P Sandipan,Abhilasha Narote,Laxman Narote,Waghmare (2009). Iris Based Recognition System using Wavelet Transform.
  9. A Jain,R Bolle,S Pankanti (1999). Biometrics: Personal Identification in a Networked Society.
  10. John Daugman (2003). The importance of being random: statistical principles of iris recognition.
  11. R Wildes (1997). Iris recognition: An emerging biometric technology.
  12. W Boles,B Boashash (1998). A human identification technique using images of the iris and wavelet transform.
  13. S Lim,K Lee,O Byeon,T Kim (2001). Efficient iris recognition through improvement of feature vector and classifier.
  14. S Noh,K Pae,C Lee,J Kim (2002). Multiresolution independent component analysis for iris identification.
  15. C Tisse,L Martin,L Torres,M Robert (2002). Person identification technique using human iris recognition.
  16. L Ma,T Tan,Y Wang,D Zhang (2003). Personal Recognition Based on Iris Texture Analysis.
  17. Ahmad Sarhan (2009). Iris Recognition Using Discrete Cosine Transform and Artificial Neural Networks.
  18. Somnath Dey,Debasis Samanta (2008). Improved Feature Processing for Iris Biometric Authentication System.
  19. P Manikandan,M Sundararajan (2010). Discrete Wavelet Features Extraction For Iris Recognition Based Bio Metric Security.
  20. C Anand,Deva Durai,M Karnan (2010). Iris Recognition Using Modified Hierarchical Phase-Based Matching (Hpm) Technique.
  21. Danilo Naveed Ur Rehman,Mandic (2010). Empirical Mode Decomposition For Trivariate Signals.
  22. N Huang,Z Shen,S Long,M Wu,H Shih,Q Zheng,N Yen,C Tung,H Liu (1998). The Empirical Mode Decomposition And Hilbert Spectrum For Non-Linear And Non-Stationary Time Series Analysis.
  23. Norden Huang,Samuel Shen (2005). Hilbert-Huang Transform and Its Applications.
  24. N Huang,Z Wu (2008). A Review on Hilbert-Huang Transform: Method and Its Applications to Geophysical Studies.
  25. D Duffy (2004). The Application Of Hilbert-Huang Transforms To Meteorological Datasets.
  26. Z Wu,N Huang (2009). Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method.
  27. Danilo Mandic,Vanessa Goh (2009). Complex Valued Nonlinear Adaptive Filters.
  28. Jen-Chun Lee,Ping Huang,Chung-Shi Chiang,T-M Tu,Chien-Ping Chang (2006). An Empirical Mode Decomposition Approach for Iris Recognition.
  29. Kazuyuki Miyazawa,Koichi Ito,Takafumi Aoki,Koji Kobayashi,Atsushi Katsumata (2006). An Iris Recognition System Using Phase-Based Image Matching.
  30. Jen-Chun Lee,Ping Huang,Te-Ming Tu,Chien-Ping Chang (2007). Recognizing Human Iris By Modified Empirical Mode Decomposition.
  31. K Miyazawa,K Ito,T Aoki,K Kobayashi,H Nakajima (2008). An Effective Approach for Iris Recognition Using Phase-Based Image Matching.
  32. S Arivazhagan,L Ganesan,T Srividya (2009). Iris recognition using multi-resolution transforms.
  33. Chung-Chih Tsai Heng,Yi,Lin Jinshiuh,Taur Chin,Wang Tao (2010). A New Matching Approach For Local Feature Based Iris Recognition Systems.
  34. Debnath Bhattacharyya,Poulami Das,Samir Kumar Bandyopadhyay,Tai-Hoon Kim (2008). IRIS Texture Analysis and Feature Extraction for Biometric Pattern Recognition.
  35. J Daugman (2001). Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns.
  36. S Maheswari,P Anbalagan,T Priya (2008). Efficient Iris Recognition through Improvement in Iris Segmentation Algorithm.
  37. Christopher Bishop (1996). Neural Networks.
  38. (2005). Database of 756 Gray scale Eye Images.
  39. John Daugman (2002). How Iris Recognition Works.
  40. L Ma,Y Wang,T Tan (2002). Iris Recognition Using Circular Symmetric Filters.
  41. Jyh-Chian Chang,Ming-Yu Huang,Jen-Chun Lee,Chien-Ping Chang,Te-Ming Tu (2009). Iris recognition with an improved empirical mode decomposition method.
  42. Y Zhu,T Tan,Y Wang (2000). Biometric Personal Identification Based On Iris Pattern.
  43. N Huang,Z Shen,S Long (1998). The Empirical Mode Decomposition And Hilbert Spectrum For Nonlinear And Nonstationary Time Series Analysis.
  44. S Long,N Huang,C Tung,M Wu,R Lin,E Mollo-Christensen,Y Yuan (1995). The Hilbert Techniques: An Alternate Approach For Non-Steady Time Series Analysis.
  45. J Canny (1986). A Computational Approach to Edge Detection.
Show All References
Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr, Deepak Sharma. 1970. "AN EFFECTIVE BLOCK WEIGHTAGE BASED TECHNIQUE FOR IRIS RECOGNITION USING EMPIRICAL MODE DECOMPOSITION". Global Journal of Computer Science and Technology GJCST Volume 11 (GJCST Volume 11 Issue 4).

More Citation Formats

Download Citation

  • Open Access
  • CC-BY-ND-NC 4.0
Download Article
Access OJS
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Not Found
Classification
GJCST Classification I.4.8
  • Submission ReceivedJanuary 20, 2011
  • Peer Review Double Blind
    Handling Editor
  • Accepted February 16, 2011
  • Published March 3, 2011
Version of record

v1.2

Issue date
March 13, 2011

Language
English
Order Article Reprint
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR
Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D
Article Matrices
Total Views: 6.2K
Total Downloads: 401
All Trends

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

AN EFFECTIVE BLOCK WEIGHTAGE BASED TECHNIQUE FOR IRIS RECOGNITION USING EMPIRICAL MODE DECOMPOSITION

Dr, Sharma
Dr, Sharma Kurukshetra University
  • Submission ReceivedJanuary 20, 2011
  • Peer Review Double Blind
    Handling Editor
  • Accepted February 16, 2011
  • Published March 3, 2011