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BIOMETRICS FUSION FOR IMPROVED VERIFICATION
1
Author(s):
ANU
Vol - 4, Issue- 2 ,
Page(s) : 8 - 17
(2014 )
DOI : https://doi.org/10.32804/RJSET
Abstract
A Biometrics technology is used to conduct the genuineness of identification and authenticity as per the physiological or behavioural statistics. Biometric is a combination of two words „Bio‟ implies physiological aspects, whereas „Metric‟ refers to measure characteristics i.e. personal identification data. Besides safe and convenient, this technology is very reliable and secure. Biometric is very important in today’s world of fast development in computer technology. Despite considerable advances in recent years, there are still challenges in authentication based on a single biometric trait, such as noisy data, restricted degree of freedom, intra-class variability, non-universality, spoof attack and undesirable error rates. Some of the restrictions can be lifted by designing a multimodal biometric system. Multimodal biometrics provides ultra-secure authentication using multiple biometric traits. We discuss here different types of multimodal biometric systems, different decision fusion techniques used in these systems.
- W. Zhao, R. Chellapa, P. J. Phillips, and A. Rosenfield, “Face recognition: A literature survey”, ACM Computing Surveys, vol. 35, pp.399-458, 2003.
- L. Shen and L. Bai, “A review on Gabor wavelets for face recognition”,Pattern Analysis and Applications, vol. 9, pp. 273-292, 2006.
- Y. Sui, X. Zou, and Y. Du, “Biometrics-based Authentication: a New Approach”, Biometric Security and Online Application, The International Conference on Computer Communications and Networks (ICCCN 2011), Maui, Hawaii, USA, Aug 03 2011.
- L. Hong and A. Jain, “Integrating faces and fingerprints for personalidentification”, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 20, pp. 1295-1307, 1998.
- L. Zhang, D. Zhang, and H. Zhu, “Online finger-knuckle-print verification for personal authentication”, Pattern Recognition, vol. 43, pp. 2560-2571, 2010.
- http://dspace.univ tlemcen.dz/bitstream/112/2435/1/DEMRI-mohammed.pdf.
- N. Samoska “Evaluation and Performance prediction of multimodal biometric systems” 2006.
- De-Song Wang, Jian-Ping Li, Yue-Hao Yan “A Novel Authentication scheme of the DRM System based on Multimodal Biometric Verification and Watermarking Technique”, Apperceiving Computing and Intelligence Analysis (ICACIA), pp. 212-215, 2008.
- Kang Ryoung Park, Dae Sik Jeong, Byung Jun Kang, and Eui Chul Lee, "A Study on Iris Feature Watermarking on Face Data", ICANNGA 2007, Part II, LNCS 4432, pp. 415-423, 2007.
- Zhenan Sun, “Improving Iris Recognition Accuracy Via Cascaded Classifiers”, IEEE Transactions on systems, man, and cybernetics—part C:applications and reviews, 1094-6977© 2005 IEEE.
- K. Kim, “Face Recognition using Principal Component Analysis” National Institute of Technology, Rourkela, India, 2008.
- A. S. Tuama, “Iris Image Segmentation and Recognition”, International Journal of Computer Science on Emerging Technologies, vol-3, No. 2, 2012.
- D. K. Shau, M. Parsai, “Different Image Fusion Techniques: A Critical Review”, International Journal of Modern Engineering Research (IJMER), Vol. 2, Issue. 5, pp-4298-4301, 2012.
- J. Flusser, and S. Zitova, “Image Fusion: Principles, Methods and Applications”, IASTED SPPRA, Innsbruck, Austria, 2010.
- S. Vantaram, “Multiresolution Adaptive and Progressive Gradient-based colour Image Segmentation”, Journal of Electronic Imaging 19(1), 2010.
- M. Mignotte, “A Label Field Bayesian Model and its Penalized Maximum Rand Estimator for Image Segmentation”, IEEE Transactions on Image Processing, vol-19, No. 6, 2010.
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