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IJSTR >> Volume 6 - Issue 4, April 2017 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Multimodal Biometric System:- Fusion Of Face And Fingerprint Biometrics At Match Score Fusion Level

[Full Text]

 

AUTHOR(S)

Grace Wangari Mwaura, Prof. Waweru Mwangi, Dr. Calvins Otieno

 

KEYWORDS

False Acceptance Rate (FAR), False Rejection Rate (FRR), Genuine Accept Rate (GAR), Receiver Operating Characteristics (ROC), Equal Error Rate (EER), multimodal, Unimodal, K Nearest Neighbor (KNN), scale invariant feature transform (SIFT), support vector machine (SVM)

 

ABSTRACT

Biometrics has developed to be one of the most relevant technologies used in Information Technology (IT) security. Unimodal biometric systems have a variety of problems which decreases the performance and accuracy of these system. One way to overcome the limitations of the unimodal biometric systems is through fusion to form a multimodal biometric system. Generally, biometric fusion is defined as the use of multiple types of biometric data or ways of processing the data to improve the performance of biometric systems. This paper proposes to develop a model for fusion of the face and fingerprint biometric at the match score fusion level. The face and fingerprint unimodal in the proposed model are built using scale invariant feature transform (SIFT) algorithm and the hamming distance to measure the distance between key points. To evaluate the performance of the multimodal system the FAR and FRR of the multimodal are compared along those of the individual unimodal systems. It has been established that the multimodal has a higher accuracy of 92.5% compared to the face unimodal system at 90% while the fingerprint unimodal system is at 82.5%.

 

REFERENCES

[1] Jain A . K. , Ross A., & Pankanti S., “Biometrics: A tool for information security”. IEEE Transactions on Information Forensics and Security, 2006, 125-143.

[2] K.Sasidhar, Kakulapati V. L., & Ramakrishna K., “multimodal biometric systems study to improve accuracy and performance”, International Journal of Computer Science & Engineering Survey (IJCSES) , 2010.

[3] Jain A. K., Ross A., Prabhakar S., “An Introduction to Biometric Recognition”, IEEE Transactions on Circuits and Systems for Video Technology. Special Issue on Image- and Video-Based Biometrics ,2004

[4] G. Chandran and Dr. R.S. Rajesh.” Performance Analysis of Multimodal Biometric,” IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.3, March 2009, pp 290-296

[5] Ross A., and Jain A., “Information fusion in biometrics” Pattern Recognition Lett., 2003, pp. 2115-2125.

[6] Ross A., Nandakur K., and Jain A., “Handbook of multibiometrics”, springer-verlag , 2006.

[7] Ghayoumi M., “A review of multimodal biometric systems: Fusion methods and their applications” IEEE/ACIS 14th International Conference Computer and Information Science (ICIS), Las Vegas, 2015 ,pp. 131 - 136.

[8] Anil Jain and Arun Ross., “fingerprint mosaicking . Acoust speech signal process,” In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , Orlando, Florida, May 13 - 17, 2002 (pp. 4064-4064).

[9] Rattaini A., kisku D., and Bicego, M., “ Feature level fusion of face and fingerprint biometrics” . Institute of Electrical and Electronics Engineers (IEEE). May 25, 2007

[10] Zhou X., and Bhanu B., “Feature Fusion of Face and Gait for Human Recognition at a Distance in Video”. Journal of Computational Information Systems , 2011, 5723-5731.

[11] Hassan N., Ramli D. A., and Siandi S. A. , “Fusion of face and fingerprint for robust personal verification system”, International journal of machine learning and computing , 2014

[12] Kinnunen T., Hautamaki V., and Franti P., “fusion of spectral feature set for accurate speaker identification” . 9th conf. speech computer, peterburg Russia, 2004 , pp. 361-365.

[13] Hong L., and Jain A. “intergrating face and fingerprint for personal identification” . IEEE transactions on pattent ananlysis and machine intelligence , 1998, 1295-1307.

[14] Shubhangi D. and Manohar B., “Multi-Biometric Approaches to Face and Fingerprint Biometrics”. International Journal of Engineering Research & Technology, Vol. 1 Issue 5, 2012, 2278- 0181.

[15] Mr. Amit Kr. Gautam, M. T. (2014). Improved Face Recognition Technique using Sift. Journal of Electrical and Electronics Engineering (IOSR-JEEE), pp. 72-76.

[16] Park, U., Pankanti, S., & Jain, A. K. (2008). Fingerprint Verification Using SIFT Features. SPIE Defense and Security Symposium. Orlando, Florida,.

[17] Biometric System Lab - University of Bologna., University of Bologna. Retrieved from Biometric System Lab, 2003. Accessed August 2016 http://bias.csr.unibo.it/fvc2004/download.asp

[18] AT&T Laboratories Cambridge. (2002). AT&T Laboratories Cambridge. Retrieved from The database of faces, 2002. Accessed August 2016 http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html