Abstract
The hand geometry, features in face, iris scan, and fingerprint vary from person to person, which provide unique features to be used in biometrics field for providing security to various systems. Most of the mono-biometric authentication systems give high error rate as they use only one feature. Hence, multimodal biometric systems are introduced, which can help in reducing the error rate at the cost of maintaining more data related to the features. Hence, it is said to be that the multimodal biometric systems are more reliable and secure. Image-based approaches offer much higher computation efficiency with minimum preprocessing. This approach is proved to be effective as the reliable feature extraction is possible even when the quality of image is low. However, this approach is weak if there are distortions in the shape of the image and variation in the positions or the orientation angle. Hence, this chapter presents a multimodal biometric invariant fusion authentication system based on fusion of Zφ invariant moment of fingerprint and face features. It reduces the storage of more features for authentication and reduces the error rate. The Morlet wavelet transform is used to make the system less sensitive to shape distortion by smoothening and preserving the local edges. The Zφ moment is the combination of Zernike and invariant moments, which are used to produce an affine transformation that is extracted from the fingerprint and the face. Authentication is successful if the similarity is 90% in the case of fingerprint and 70% in the case of face. False acceptance rate (FAR) and false reject rate (FRR) are optimal with these threshold values.
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References
M.S. Obaidat, N. Boudriga, Security of e-Systems and Computer Networks (Cambridge University Press, Cambridge, UK, 2007)
M.S. Obaidat, B. Sadoun, Verification of computer users using keystroke dynamics. IEEE Trans. Syst. Man Cybern. B 27(2), 261–269 (1997)
M.S. Obaidat, B. Sadoun, Keystroke dynamics based identification, in Biometrics: Personal Identification in Networked Society, ed. by A. Jain et al. (Springer, Kluwer, 1999), pp. 213–229
W. Stallings, Cryptography and Network Security- Principles and Practices (Prentice-Hall, Upper Saddle River, 2003)
T. Jea, V. Govindaraju, A minutia-based partial fingerprint recognition system. Pattern Recogn. 38(10), 1672–1684 (2005)
T. Jea, V.K. Chavan, V. Govindaraju, J.K. Schneider, Security and matching of partial fingerprint recognition systems. Proc. SPIE 5404, 39–50 (2004)
D. Maio, D. Maltoni, A.K. Jain, S. Prabhakar, Handbook of Fingerprint Recognition (Springer, Berlin, 2003)
P. Viswanathan, P. Venkata Krishna, Fingerprint enhancement and compression method using Morletwavelet. Int. J. Signal Imaging Syst. Eng. 3(4), 261–268 (2010)
S. Prabhakar, J. Wang, A. K. Jain, S. Pankanti, R. Bolle. Minutiae verification and classification for fingerprint matching. In Proc. 15th International Conference Pattern Recognition, Vol. 1, Barcelona, September 3–8, 2000, pp. 25–29
J. Liu, Z. Huang, K. Chan, Direct minutiae extraction from gray-level fingerprint image by relationship examination. Proc. Int. Conf. Image Process. 2, 427–430 (2000)
P. Viswanathan, P. Venkata Krishna, Morlet Wavelet fingerprint invariant automated authentication system. Int. J. Recent Trends Eng. 4(1), 1–5 (2010)
C. Chen, Decision level fusion of hybrid local features for face recognition. In Neural networks and signal Processing, 2008 International Conference on (pp. 199–204). IEEE (2008).
L.F. Sha, F. Zhao, X.O. Tang, Improved finger code for filter bank-based fingerprint matching. Proc. Int. Conf. Image Process. 2, 895–898 (2003)
M. Tico, E. Immonen, P. Ramo, P. Kuosmanen, J. Saarinen, Fingerprint recognition using wavelet features. Proc. IEEE Int. Symp. Circuits Syst. 2, 21–24 (2001)
T. Amornraksa, S. Achaphetpiboon, Fingerprint recognition using DCT features. Electron. Lett. 42(9), 522–523 (2006)
A.T.B. Jin, D.N.C. Ling, O.T. Song, An efficient fingerprint verification system using integrated wavelet and Fourier-Mellin invariant transform. Image Vis. Comput. 22(6), 503–513 (2004)
D. Maio, D. Maltoni, Direct gray scale minutia detection in fingerprints. Trans. PAMI 19(1), 27–40 (1997)
P. Viswanathan, P. VenkataKrishna, Multimodal biometric invariant moment fusion authentication system. Information Management Processing, BAIP 2010, Springer CCIS, vol 70, 2010, pp. 136–144
G.L. Marcialis, F. Roli, Score-level fusion of fingerprint and face matchers for personal verification under “stress” conditions. In 14th International Conference on Image Analysis and Processing (ICIAP 2007) 0-7695-2877-5/07 $25.00 © 2007 IEEE
A. Rattani, D.R. Kisku, M. Bicego, M. Tistarelli, Feature level fusion of face and fingerprint biometrics 978-1-4244-1597-7/07/$25.00 ©2007 IEEE
T.-Y. Jea, V. Govindaraju, A minutia-based partial fingerprint recognition system. Pattern Recogn. 38(10), 1672–1684 (2005)
C.I. Watson, G.T. Candela, P.J. Grother, Comparison of FFT fingerprint filtering methods for neural network classification. NISTIR 5493 (1994) Available: https://ws680.nist.gov/publication/get_pdf.cfm?pub_id=900727
M.K. Hu, Visual pattern recognition by moment invariants. IRE Trans. Info. Theory IT-8, 179–187 (1962)
J.C. Yang, D.S. Park, Fingerprint verification based on invariant moment features and nonlinear BPNN. Int. J. Control. Autom. Syst. 6(6), 800–808 (2008)
L. O’Gormann, J.V. Nickerson, An approach to fingerprint filter design. Pattern Recogn. 22(1), 29–38 (1989)
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Viswanatham, P., Venkata Krishna, P., Saritha, V., Obaidat, M.S. (2019). Multimodal Biometric Invariant Fusion Techniques. In: Obaidat, M., Traore, I., Woungang, I. (eds) Biometric-Based Physical and Cybersecurity Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-98734-7_12
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