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69. E. Maiorana, P. Campisi, and A. Neri, Biometric signature authentication using radon transform-based watermarking techniques, in IEEE Biometric Symposium, Baltimore, September 2007. 70. S. Katzenbeisser, On the integration of watermarks and cryptography, Lecture Notes Comput. Sci. 2939:50 60, 2004. 71. I. J. Cox, G. Doerr, and T. Furon, Watermarking is not cryptography, Lecture Notes Comput. Sci. 4283:1 15, 2006. 72. R. Plamondon and G. Lorette, Automatic signature veri cation and writer identi cation: The state of the art, Pattern Recognit. 22(2):107 131, 1989. 73. F. Leclerc and R. Plamondon, Automatic signature veri cation: The state of the art 1989 1993, IJPRAI 8(3):643 660, 1994. 74. R. Plamondon and S. N. Srihari, On-line and off-line handwriting recognition: A comprehensive survey, IEEE Trans. Pattern Anal. Mach. Intelli. 22(1):63 84, 2000. 75. G. Dimauro, S. Impedovo, M. G. Lucchese, R. Modugno, and G. Pirlo, Recent advancements in automatic signature veri cation, in Ninth International Workshop on Frontiers in Handwriting Recognition, 26 29 October 2004, pp. 179 184. 76. M. Faundez-Zanuy, Signature recognition state-of-the-art, IEEE Aerosp. Electron. Syst. Mag. 20(7):28 32, 2005. 77. H. Lei and V. Govindaraju, A comparative study on the consistency of features in on-line signature veri cation, Pattern Recognit. Lett. 15:2483 2489, 2005. 78. A. K. Jain, R. P. W. Duin, and J. Mao, Statistical pattern recognition: A review, IEEE Trans. Pattern Anal. Mach. Intell. 22(1):4 37, 2000. 79. Y. Jonghyon, L. Chulhan, and K. Jaihie, On-line signature veri cation using temporal shift estimated by the phase of Gabor lter, IEEE Trans. Signal Processing 53(2, Part 2):776 783, 2005. 80. M. Faundez-Zanuy, On-line signature recognition based on VQ-DTW, Pattern Recognit. 40:981 992, 2007. 81. G. Agam and S. Suresh, Warping-based off-line signature recognition, IEEE Trans. Info. Forens. Secur. 2(3, Part 1):430 437, 2007. 82. A. Kholmatov and B. Yanikoglu, Identity authentication using improved on-line signature veri cation method, Pattern Recognit. Lett. 26(15):2400-2408, 2005. 83. L. Yang, B. W. Widjaja, and R. Prasad, Application of hidden markov models for signature veri cation, Pattern Recognit. 28(2):161 170, 1995. 84. J. Fierrez, J. Ortega-Garcia, D. Ramos, and J. Gonzalez-Rodriguez, HMM-based on-line signature veri cation: Feature extraction and signature modeling, Pattern Recognit. Lett. 28(16):2325 2334, 2007. 85. M. Fuentes, S. Garcia-Salicetti, and B. Dorizzi, On-line signature veri cation: Fusion of a hidden Markov model and a neural network via a support vector machine, in Eighth International Workshop on Frontiers in Handwriting Recognition, 6 8 August 2002, pp. 253 258. 86. C. Quek and R. W. Zhou, Antiforgery: A novel pseudo-outer product based fuzzy neural network driver signature veri cation system, Pattern Recognit. 23:1795 1816, 2002. 87. N. K. Ratha, J. H. Connell, and R. Bolle, Enhancing security and privacy of biometric-based authentication systems, IBM Systems J. 40(3):614 634, 2001. 88. G. A. P. Cirrone, S. Donadio, S. Guatelli, A. Mantero, B. Mascialino, S. Parlati, M. G. Pia, A. Pfeiffer, A. Ribon, and P. Viarengo, A goodness-of- t statistical toolkit, IEEE Trans. Nuclear Sci. 51(5):2056 2063, 2004. 89. M. Purser, Introduction to Error-Correcting Codes, Artech House, Boston, 1995. 90. Federal Information Processing (FIP) Standards Publication 180-1, Security Hash Standard, pspubs/ p180-1.htm, 1995. 91. V. Rijmen and E. Oswald, Update on SHA-1, Lecture Notes Comput. Sci. 3376:58 71, 2005. 92. X. Wang, Y. L. Yin, and H. Yu, Finding Collisions in the Full SHA-1, CRYPTO, 2005. 93. C. De Canni re and C. Rechberger, Finding SHA-1 characteristics: General results and applications, in Advances in Cryptology, ASIACRYPT, Vol. 4284, Springer, Berlin, 2006. 94. A. A. Ross, K. Nandakumar, and A. K. Jain, Handbook of Multibiometrics, Springer 2006, USA.
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95. M. Faundez-Zanuy, Data fusion in biometrics, IEEE Aerosp. Electron. Syst. Mag. 20(1):34 38, 2005. 96. J. Fierrez-Aguilar, L. Nanni, J. Lopez-Pe alba, J. Ortega-Garcia, and D. Maltoni, An on-line signan ture veri cation system based on fusion of local and global information, in International Conference on Audio and Video-Based Biometric Person Authentication, AVBPA, 2005, pp. 523 532. 97. B. Chen and G. Wornell, Quantization index modulation: A class of provably good methods for digital watermarking and information embedding, IEEE Trans. Info. Theory 47(4):1423 1443, 2001. 98. S. R. Deans, The Radon Transform and Some of Its Applications, John Wiley & Sons, New York, 1983. 99. M. N. Do and M. Vetterli, The nite ridgelet transform for image representation, IEEE Trans. Image Processing 12(1):16 28, 2003. 100. P. Campisi, D. Kundur, and A. Neri, Robust digital watermarking in the ridgelet domain, IEEE Signal Processing Lett. 11(10), 2004. 101. L. Mao, H.-Z. Wu, Z.-H. Wei, and Y. Bao, Perceptual digital watermark of images using ridgelet transform, in Third International Conference on Machine Learning and Cybernetics, Shanghai, 2004.
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