INFORMATION CHANGE THE WORLD

International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.11, No.4, Apr. 2019

Wavelet based Multimodal Biometrics with Score Level Fusion Using Mathematical Normalization

Full Text (PDF, 533KB), PP.63-71


Views:0   Downloads:0

Author(s)

Priti S. Sanjekar, J. B. Patil

Index Terms

Multimodal biometrics;Matching score level fusion;mathematical normalization;wavelet;fingerprint;palmprint;iris

Abstract

Biometric based authentication is playing a very important role in various security related applications. A novel multimodal biometric verification based on fingerprint, palmprint and iris with matching score level fusion using Mathematical Normalization is proposed in this paper. In feature extraction stage of unimodal, features of each modality are extracted by applying wavelet decomposition using 6 different wavelet families and 35 respective wavelet family members. Further, the three optimal combinations of unimodal systems based on equal error rate achieved by wavelet(s) are chosen for development of multimodal biometric system. In matching score level fusion, along with well-known normalization techniques- Min-max, Tan-h and Z-score, the performance of multimodal systems are also analyzed using Mathematical Normalization (Math-norm) followed by product, weighted product, sum and average fusion rule. The experiments are conducted on database of 100 different subjects from publically available FVC2006, CASIA V1 and IITD database of fingerprint, palmprint and iris, respectively. The experimental results clearly show that Mathematical Normalization followed by weighted product has given promising accuracy with equal error rate (EER) of 0.325%.

Cite This Paper

Priti S. Sanjekar, J. B. Patil, " Wavelet based Multimodal Biometrics with Score Level Fusion Using Mathematical Normalization", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.4, pp. 63-71, 2019.DOI: 10.5815/ijigsp.2019.04.06

Reference

[1]J. A. Unar, W. C. Seng and A. Abbasi, “A Review of Biometric Technology Along with Trends and Prospects,” Pattern Recognition, vol. 47, no. 8, pp. 2673-2688, August 2014.

[2]M. Hanmandlu, J. Grover, A. Gureja and H. M. Gupta, “Score level fusion of multimodal biometrics using triangular norms,” Pattern Recognition Letters, vol. 32, no. 14,  pp.1843–1850,  2011.

[3]A. K. Jain, A. Ross and S. Prabhakar, “An Introduction to Biometric Recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp.4–19, 2004.

[4]A. K. Jain, P. Flynn and A. A. Ross, “Handbook of Multibiometric,” Springer, N J, USA, pp. 1–22, 2008.

[5]J. Peng, A. Ahmed, and X. Niu, “Multimodal biometric authentication based on score level fusion of finger biometrics,” Elsevier, Optik, vol. 125, pp. 6891–6897, 2014.

[6]M. He, S. Horng, P. Fan, R. Run, R. Chen, J. L. Lai, M. K. Khan and K. O. Sentosa, “Performance evaluation of score level fusion in multimodal biometric systems,” Pattern Recognition, vol. 43, pp.1789–1800,  2010.

[7]A. Poursaberi  and B. N. Arabi “A novel IRIS recognition system using morphological edge detector and wavelet phase features,” Journal of Graphics, Vision and Image Processing, vol.6, pp. 9-15, 2005.

[8]P. S. Sanjekar and J. B. Patil, “An Overview of Multimodal Biometrics,” Signal and Image Processing: An International Journal (SIPIJ), vol. 4, no.1, pp. 57-64, Feb 2013.

[9]S. Sharma, S. R. Dubey, S. K. Singh, R. Saxena and R. Singh , “Identity verification using shape and geometry of human hands,” Expert Systems with Applications, vol. 42, pp. 821–832,  2015.

[10]S. Ribaric and I. Fratric, “Experimental Evaluation of Matching-Score Normalization Techniques on Different Multimodal Biometric Systems,” Proc. IEEE Mediterranean Electrotechnical Conference, May 2006, pp. 498 – 501.

[11]T. A. Alghamdi, “Evaluation of Multimodal Biometrics at Different Levels of Face and Palm Print Fusion Schemes,” Asian Journal of Applied Sciences, vol.9, no.2 pp.126-130, 2016.

[12]M. I. Razzak, R. Yusof and M. Khalid, “Multimodal face and finger veins biometric authentication,” Scientific Research and Essays, vol. 5, no.17, pp. 2529-2534, Sep. 2010.

[13]A. P. Yazdanpanah , K. Faez and R. Amirfattahi, “ Multimodal Biometric System using Face, Ear and Gait Biometrics,” Proc.10th International IEEE Conference on Information Science, Signal Processing and their Applications (ISSPA), May 2010, pp.251-254.

[14]A. B. Khalifa, “ Adaptive Score Normalization: A Novel approach for Multimodal Biometric Systems,” World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering, vol. 7, no. 3, pp.376-384, 2013.

[15]A. Kumar, D. Zhang, “Combining Fingerprint, Palmprint and Hand-Shape for User Authentication,” Proc.18th International Conference on Pattern Recognition (ICPR), Aug. 2006, pp. 549-553.

[16]M. Hanmandlu, J. Grover, A. Gureja and H.M. Gupta, “Score level fusion of multimodal biometrics using triangular norms,” Pattern Recognition Letters, vol.32, pp.1843–1850, 2011.

[17]K. Nandakumar, Y. Chen, A.K. Jain and S.C. Dass, “Quality based Score Level Fusion in Multibiometric systems,” Proc. 18th International Conference on Pattern Recognition (ICPR), 2006, pp. 473–476.

[18]A. Jain, K. Nandakumar and A. Ross, “Score normalization in multimodal biometric systems,” Pattern Recognition, vol. 38, pp. 2270 – 2285, 2005.

[19]R. Raghavendra, B. Dorizzi, A. Rao and G.H. Kumar, “Designing efficient fusion schemes for multimodal biometric systems using face and palmprint,” Pattern Recognition, vol. 44, pp.1076–1088, 2011.

[20]F. F. Cui, and G. P. Yang, “Score Level Fusion of Fingerprint and Finger Vein Recognition,” Journal of Computational Information Systems, vol. 16, pp. 5723–5731, 2011.

[21]M. S. M. Asaari, S. A. Suandi and B. A. Rosdi, “Fusion of Band Limited Phase Only Correlation and Width Centroid Contour Distance for finger based biometrics,” Expert Systems with Applications, vol. 41, pp. 3367–3382, 2014.

[22]P. S. Sanjekar and P. S. Dhabe, “Fingerprint Verification Using Haar Wavelet”, Proc. IEEE second International Conference on Computer Engineering and Technology (ICCET), vol. 3 no.1, 2010, pp.361-365.

[23]P. S. Sanjekar, P. D. Saraf and B. D. Patil, “Review on Core Point Detection Techniques in Fingerprint,” International Journal Computer Applications, no.3, pp.18-20, Dec. 2014.  

[24]P. Mohanaiah, P. Sathyanarayana and L. GuruKumar, “Image Texture Feature Extraction Using GLCM Approach”, International Journal of Scientific and Research Publications. vol. 3 no. 5, pp. 1-5, May 2013.

[25]R. M. Rao and A. S. Bopardikar, “Wavelet Transforms: Introduction to theory and Applications,” Addison Wesley Longman, Inc., Reading, 1998.

[26]G. Bhatnagar, Q. M. J. Wu and B. Raman, “A New Fractional Random Wavelet Transform for Fingerprint Security,” IEEE Trans. On Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 42, no. 1, pp. 262-275, Jan. 2012.

[27]P. S. Sanjekar and J. B. Patil, “Method of ROI Extraction for Palmprint,” Indian Patent Application No. 201621044219 A, Filed on 26th Dec., 2016, Published on 24th Feb., 2017.

[28]Iris database: http://web.iitd.ac.in/~biometrics/Database_Iris.html

[29]J. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 15, pp. 1148–1161, Nov. 1993.

[30]S.C. Dass, K. Nandakumar and A.K. Jain, “A principled approach to score level fusion in multimodal biometric systems,” Proc. 5th International Conference on Audio and Video Based Personal Authentication (AVBPA), July 2005, pp. 1049–1058.

[31]R. O. Duda, P. E. Hart and D. G. Stork, “Pattern Classification,” John Wiley & Sons, New York, 2001.

[32]http://people.revoledu.com/kardi/tutorial/ Similarity/Normalization.html

[33]Fingerprint database: http://atvs.ii.uam.es/atvs/fvc2006.html

[34]Palmprint database: http://biometrics.idealtest.org

[35]V. Kanhangad, A. Kumar and D. Zhang, “A Unified Framework for Contactless Hand Verification,” IEEE Trans. on information forensics and and Security, vol.6, no.3, Sep. 2011.