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Score Level Fusion for Fingerprint, Iris and Face Biometrics

by Ashraf Aboshosha, Kamal A. El Dahshan, Eman A. Karam, Ebeid A. Ebeid
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 4
Year of Publication: 2015
Authors: Ashraf Aboshosha, Kamal A. El Dahshan, Eman A. Karam, Ebeid A. Ebeid
10.5120/19530-1171

Ashraf Aboshosha, Kamal A. El Dahshan, Eman A. Karam, Ebeid A. Ebeid . Score Level Fusion for Fingerprint, Iris and Face Biometrics. International Journal of Computer Applications. 111, 4 ( February 2015), 47-55. DOI=10.5120/19530-1171

@article{ 10.5120/19530-1171,
author = { Ashraf Aboshosha, Kamal A. El Dahshan, Eman A. Karam, Ebeid A. Ebeid },
title = { Score Level Fusion for Fingerprint, Iris and Face Biometrics },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 4 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 47-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume111/number4/19530-1171/ },
doi = { 10.5120/19530-1171 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:47:01.768564+05:30
%A Ashraf Aboshosha
%A Kamal A. El Dahshan
%A Eman A. Karam
%A Ebeid A. Ebeid
%T Score Level Fusion for Fingerprint, Iris and Face Biometrics
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 4
%P 47-55
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Single biometric systems suffer from many challenges such as noisy data, non-universality and spoof attacks. Multimodal biometric systems can solve these limitations effectively by using two or more individual modalities. In this paper fusion of fingerprint, iris and face traits are used at score level in order to improve the accuracy of the system. Scores which obtained from the classifiers are normalized first using min-max normalization. Then sum, product and weighted sum rules are used to get fusion. Experimental results show that multimodal biometric systems outperform unimodal biometric systems and weighted sum rule gives the best results comparing with sum or product method.

References
  1. R Raghavendra, Rao Ashok, and G Hemantha Kumar. 2010. Multimodal biometric score fusion using gaussian mixture model and monte carlo method. Journal of Computer Science and Technology, 25(4):771–782.
  2. Houda Benaliouche and Mohamed Touahria. 2014 Comparative study of multimodal biometric recognition by fusion of iris and fingerprint. The Scientific World Journal, 2014.
  3. A run Ross and Anil Jain. 2004 Multimodal biometrics: An overview. na.
  4. Maryam Eskandari and O¨ nsen Toygar. 2012 Fusion of face and iris biometrics using local and global feature extraction methods. Signal, Image and Video Processing, pages 1–12.
  5. Fan Yang and Baofeng Ma. 2007. A new mixed-mode biometrics information fusion based-on fingerprint, hand-geometry and palm-print. In Image and Graphics, 2007. ICIG 2007. Fourth International Conference on, pages 689–693. IEEE.
  6. Mohamad Abdolahi, Majid Mohamadi, and Mehdi Jafari. 2013. Multimodal biometric system fusion using fingerprint and iris with fuzzy logic. International Journal of Soft Computing and Engineering, 2(6):504–510.
  7. Byungjun Son and Yillbyung Lee. 2005. Biometric authentication system using reduced joint feature vector of iris and face. In Audio-and Video-Based Biometric Person Authentication, pages 513–522. Springer.
  8. Calvin R Maurer and J Michael Fitzpatrick. 1993 A review of medical image registration. Interactive image-guided neurosurgery, 17, 1993.
  9. Ajita Rattani and Massimo Tistarelli. 2009 Robust multi-modal and multiunit feature level fusion of face and iris biometrics. In Advances in Biometrics, pages 960–969. Springer.
  10. L Latha and S Thangasamy. 2010. A robust person authentication system based on score level fusion of left and right irises and retinal features. Procedia Computer Science, 2:111–120.
  11. Davide Maltoni, Dario Maio, Anil K Jain, and Salil Prabhakar. 2009 Handbook of fingerprint recognition. Springer.
  12. PP Chitte, JG Rana, RR Bhambare, VA More, RA Kadu, and MR Bendre. 2012. Iris recognition system using ica, pca, daugmans rubber sheet model together. International Journal of Computer Technology and Electronics Engineering, 2(1):16–23.
  13. Sunil Chawla and Aashish Oberoi. 2011 A robust algorithm for iris segmentation and normalization using hough transform. Global Journal of Business Management and Information Technology, 1(2):69–76.
  14. Zhenhua Guo, Lei Zhang, and David Zhang. 2010 Rotation invariant texture classification using lbp variance (lbpv) with global matching. Pattern recognition, 43(3):706–719.
  15. Anil K Jain, Arun Ross, and Salil Prabhakar. 2004 An introduction to biometric recognition. Circuits and Systems for Video Technology, IEEE Transactions on, 14(1):4–20.
  16. Philippe Parra. Fingerprint minutiae extraction and matching for identification procedure. University of California, San Diego La Jolla, CA, pages 92093–0443.
  17. BG Sherlock, DM Monro, and K Millard. 1994 Fingerprint enhancement by directional fourier filtering. In Vision, Image and Signal Processing, IEEE. Proceedings-, volume 141, pages 87–94. IET.
  18. Wuzhili. 2002 fingerprint recognition.
  19. Heng Fui Liau and Dino Isa. 2011 Feature selection for support vector machine-based face-iris multimodal biometric system. Expert Systems with Applications, 38(9):11105–11111.
  20. Hugo Proenc¸a and Lu?s A Alexandre. 2006. Iris recognition: An analysis of the aliasing problem in the iris normalization stage. In Computational Intelligence and Security, 2006 International Conference on, volume 2, pages 1771–1774. IEEE.
  21. Sunil Chawla and Aashish Oberoi. , 2011. Robust algorithm for iris segmentation and normalization using hough transform. Global Journal of Business Management and Information Technology, 1:69–76.
  22. Maeva Djoumessi. Iris segmentation & recognition.
  23. John Daugman. How iris recognition works. 2004 Circuits and Systems for Video Technology, IEEE Transactions on, 14(1):21–30.
  24. Chong Siew Chin, Andrew Teoh Beng Jin, and David Ngo Chek Ling. , 2006 High security iris verification system based on random secret integration. Computer Vision and Image Understanding, 102(2):169–177.
  25. Shengcai Liao, Xiangxin Zhu, Zhen Lei, Lun Zhang, and Stan Z Li. 2007. Learning multi-scale block local binary patterns for face recognition. In Advances in Biometrics, pages 828–837. Springer.
  26. Alper Yilmaz and Muhittin G¨okmen. 2001. Eigenhill vs. eigenface and eigenedge. Pattern Recognition, 34(1):181–184.
  27. Peter N. Belhumeur, Jo˜ao P Hespanha, and David Kriegman. 1997 Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(7):711–720. .
  28. Chengjun Liu and Harry Wechsler. , 2002 Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. Image processing, IEEE Transactions on, 11(4):467–476.
  29. Timo Ojala, Matti Pietik¨ainen, and David Harwood. , 1996 A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1):51–59.
  30. Timo Ojala, Matti Pietikainen, and Topi Maenpaa. 2002 Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971–987.
  31. Xianbiao Qi, Yu Qiao, Chun-Guang Li, and Jun Guo. 2013 Multi-scale joint encoding of local binary patterns for texture and material classification.
  32. Timo Ahonen, Abdenour Hadid, and Matti Pietik¨ainen. 2004 Face recognition with local binary patterns. In Computer vision-eccv 2004, pages 469–481. Springer.
  33. Prachi pooja godi smita thakre, kalyani. 2012. multimodal biometric feature based person classification. international journal of omputer application.
Index Terms

Computer Science
Information Sciences

Keywords

Fusion multimodal fingerprint recognition iris recognition face recognition.

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