ABSTRACT
Biometric verification systems have to address many practical requirements, such as performance, presentation attack detection (PAD), large population coverage, demographic diversity, and varied deployment environment. Traditional unimodal biometric systems do not fully meet the aforementioned requirements making them vulnerable and susceptible to different types of attacks. In response to that, modern biometric systems combine multiple biometric modalities at different fusion levels, such as sensor, feature, score and decision level. The fused score is decisive to classify an unknown user as a genuine or impostor. In this paper, we describe a new biometric fusion framework based on Subjective Logic (SL); a type of probabilistic logic that explicitly takes uncertainty and trust into consideration. We principally evaluate our proposed fusion framework using two modalities, namely iris and fingerprint. Furethermore, the individual scores obtained from various comparators are combined at score level by applying four score fusion approaches (minimum score, maximum score, simple sum, and subjective logic) and three score normalization techniques (min-max, z-score, hyperbolic tangent). The experimental results show that the proposed score level fusion approach (subjective logic) gives the best authentication accuracy even when particular biometric classifiers give distinct comparison scores.
- B. Bhanu and V. Govindaraju. 2011. Multibiometrics for Human Identification. Cambridge University Press. https://books.google.no/books?id=aqXD3iBuQewC Google ScholarDigital Library
- Chinese Academy of Sciences. 2009. CASIA-Lamp Image Database V4.0. Technical Report. http://biometrics.idealtest.org/dbDetailForUser.do?id=4Google Scholar
- Vincenzo Conti, Giovanni Milici, Patrizia Ribino, Filippo Sorbello, and Salvatore Vitabile. 2007. Fuzzy Fusion in Multimodal Biometric Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, 108--115. Google ScholarDigital Library
- Mohammad Derawi, Davrondzhon Gafurov, and Rasmus Larsen. 2010. Fusion of Gait and Fingerprint For User Authentication on Mobile Devices. (IWSCN), 2010 2nd (2010). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5497989Google Scholar
- Anil Jain, Karthik Nandakumar, and Arun Ross. 2005. Score Normalization in Multimodal Biometric Systems. Pattern Recognition 38, 12 (dec 2005), 2270--2285. Google ScholarDigital Library
- Anil K Jain, R. Bolle, S. Pankanti, Arun A Ross, and Karthik Nandakumar. 2011. Introduction to Biometrics. Springer. Google ScholarDigital Library
- Anil K. Jain and Arun Ross. 2004. Multibiometric Systems. Commun. ACM 47, 1 (jan 2004), 34. Google ScholarDigital Library
- A. Jøsang. 2008. Conditional Reasoning with Subjective Logic. Journal of Multiple-Valued Logic and Soft Computing 15, 1 (2008), 5--38.Google Scholar
- Audun Jøsang. 2016. Subjective Logic: A Formalism For Reasoning Under Uncertainty. Springer, Heidelberg. Google ScholarCross Ref
- Audun Jøsang and Robin Hankin. 2012. Interpretation and Fusion of Hyper-Opinions in Subjective Logic. In Proceedings of the 15th International Conference on Information Fusion (FUSION 2012). IEEE, Los Alamitos.Google Scholar
- Dakshina Ranjan Kisku, P Gupta, H Mehrotra, and J Sing. 2009. Multimodal Belief Fusion for Face and Ear Biometrics. Intelligent Information Management 01, December (2009), 166--171.Google ScholarCross Ref
- L. I. Kuncheva, C. J. Whitaker, C. A. Shipp, and R. P. W. Duin. 2000. Is Independence Good For Combining Classifiers?. In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, Vol. 2. 168--171.Google ScholarCross Ref
- CW Lau, Bin Ma, HML Meng, YS Moon, and Yeung Yam. 2004. Fuzzy Logic Decision Fusion in a Multimodal Biometric System. Interspeech (2004).Google Scholar
- Davide Maltoni, Dario Maio, Anil K A.K. Jain, and Salil Prabhakar. 2009. Handbook of Fingerprint Recognition (2nd ed.). Number ISBN: 978-1-84882-253-5. Springer-Verlag. Google ScholarDigital Library
- N. Morizet and J. Gilles. 2008. A New Adaptive Combination Approach to Score Level Fusion for Face and Iris Biometrics Combining Wavelets and Statistical Moments. In Proceedings of the 4th International Symposium on Visual Computing (Advances in Visual Computing) (LNCS), G. Bebis (Ed.), Vol. 5359. 661--671. Google ScholarDigital Library
- Neurotechnology. 2017. MegaMatcher Automated Biometric Identification System for national-scale projects. (2017). http://www.neurotechnology.com/megamatcher-abis.htmlGoogle Scholar
- Jialiang Peng, Ahmed A. Abd El-Latif, Qiong Li, and Xiamu Niu. 2014. Multimodal Biometric Authentication Based on Score Level Fusion of Finger Biometrics. Optik - International Journal for Light and Electron Optics 125, 23 (dec 2014), 6891--6897.Google ScholarCross Ref
- R Raghavendra, A Rao, and G Hemantha Kumar. 2009. A Novel Approach for Multimodal Biometric Score Fusion Using Gaussian Mixture Model and Monte Carlo Method. Advances in Recent Technologies in Communication and Computing, 2009. ARTCom '09. International Conference on (2009), 90--92. Google ScholarDigital Library
- A.A. Ross, K. Nandakumar, and A.K. Jain. 2006. Handbook of Multibiometrics. Springer Science and Business Media. Google ScholarDigital Library
- Arun A Ross, Karthik Nandakumar, and Anil K Jain. 2006. Handbook of Multibiometrics (1st ed.). Number ISBN-13: 978-0-387--22296-7. Springer-Verlag. Google ScholarDigital Library
- ISO Standards. {n.d.}. ISO/IEC JTC 1/SC 37 Biometrics: SC 37 Standing Document 11 (SD 11), Part 1 Harmonization Document.Google Scholar
- K. Vishi and S.Y. Yayilgan. 2013. Multimodal Biometric Authentication Using Fingerprint and Iris Recognition in Identity Management. In Proceedings - 2013 9th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2013. Google ScholarDigital Library
- P. Walley. 1996. Inferences from Multinomial Data: Learning about a Bag of Marbles. Journal of the Royal Statistical Society 58, 1 (1996), 3--57.Google Scholar
- Franziska Wolf, Tobias Scheidat, and Claus Vielhauer. 2006. Study of Applicability of Virtual Users in Evaluating Multimodal Biometrics. Springer, Berlin, Heidelberg, 554--561. Google ScholarDigital Library
- Yilong Yin, Lili Liu, and Xiwei Sun. 2011. SDUMLA-HMT: A Multimodal Biometric Database. Biometric Recognition (2011), 260--268. http://www.springerlink.com/index/WLW15R838508UV51.pdf Google ScholarDigital Library
Index Terms
- A new approach for multi-biometric fusion based on subjective logic
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