Abstract
In this paper, we present the biometric authentication system based on the fusion of two user-friendly biometric modalities: Iris and Face. Using one biometric feature can lead to good results, but there is no reliable way to verify the classification. In order to reach robust identification and verification we are combining two different biometric features. we specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from iris and face. And then to obtain Reduced Joint Feature Vector(RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. This system can operate in two modes: to identify a particular person or to verify a person’s claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.
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References
Wang, Y., Tan, T., Jain, A.K.: Combining Face and Iris Biometrics for Identity Verification. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, Springer, Heidelberg (2003)
Jain, A.K., Bolle, R., Pankanti, S.: Biometrics- Personal Identification in Newroked Society. Kluwer Academic Publishers, Dordrecht (1999)
Ross, A., Jain, A.K.: Information fusion in biometrics. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, p. 354. Springer, Heidelberg (2001)
Zuev, Y., Ivanon, S.: The voting as a way to increase the decision reliability. In: Faundations of Information/Decision Fusion with Applications to Engineering Problems, Washington D.C., USA (1996)
Jain, A.K., Prabhakar, S., Chen, S.: Combining multiple matchers for a high security fingerprint verification system. Pattern Recognition Letters 20, 1371–1379 (1999)
Yang, J., Yu, H.: A Direct LDA Algorithm for High-Dimensional Data – with Application to Face Recognition. Pattern Recognition 34(10), 2067–2070 (2001)
Son, B., Kee, G., Byun, Y., Lee, Y.: Iris Recognition System Using Wavelet Packet and Support Vector Machines. In: Proceeding of International Workshop on Information Security Applications, Jeju, Korea (2003)
Daugman, J.G.: High confidence visual recognition of persons by a test of statistical indenpendence. IEEE Trans. On Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)
AT&T Laboratories Cambridge. The ORL Database of Faces, http://www.cam-orl.co.uk/facedatabase.html
Laboratories of Intelligent Systems, Institute of Information Science. The IIS Face Database, http://smart.iis.sinica.edu.tw/index.html
Son, B., Ahn, J., Park, J., Lee, Y.: Identification of Humans using Robust Biometrics Features. In: Proceeding of Joint International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, Lisbon, Portugal (2004)
Strang, G., Nguyen, T.Q.: Wavelets and Filter Banks. Wellesley-Cambridge Press (1998)
Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. Pattern Recognition and Machine Intelligence 11(4), 674–693 (1989)
Swets, D., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(8), 831–836 (1996)
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Son, B., Lee, Y. (2005). Biometric Authentication System Using Reduced Joint Feature Vector of Iris and Face. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_53
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DOI: https://doi.org/10.1007/11527923_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
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