We have addressed a novel watermarking algorithm to support the capacity demanded by the multimodal biometric templates. Proposed technique embeds watermark in low frequency AC coefficients of selected 8？×？8 DCT blocks. Selection of blocks accomplishes perceptual transparency by exploiting the masking effects of human visual system (HVS). Embedding is done by modulating the coefficient magnitude as a function of its estimated value. Neighborhood estimation is used for the weighted DC coefficients from eight neighboring DCT blocks. The weights of the DC coefficients are calculated from local image intrinsic property. For our experimentation we have used iris and finger prints as the two templates which are watermarked into standard test images. The robustness of the proposed algorithm is compared with the few state-of-the-art literature when watermarked image is subjected to common channel attacks. 1. Introduction With the current advances in information communication, world-wide-web connectivity, the security and privacy issues for authentication have increased by many folds. Applications such as electronic banking, e-commerce, m-commerce, ATM, smart cards, and so forth require high attention of data security, either while data is stored in the database/token, or transmitted over the network. This makes implementation of automatic, robust, and secure person identification a hot research topic. Biometric recognition offers a consistent solution for the user authentication to identity management systems. One of the reasons for popularity of this biometric system is its ability to differentiate between authorized person and forger who might illegally attempt to access the privilege of authorized person [1]. System accuracy depends on how efficiently it accepts genuine user and decline imposter user. Acceptance or denial of the user is confirmed based on matching between live and template database. However, a single physical characteristic or behavioral trait of an individual sometimes fails to stand as sufficient for user identification/verification. For this reason systems with integration of two or more different biometrics are currently have derived attention for being designed and made inter-operative. This recent development can provide an acceptable performance to increase the reliability of decisions as well as increases robustness with regard to fraudulent technologies when used by even more than one billion of users. Further it also helps to reduce failure to enroll rate (FER) or failure to capture rate (FCR) [2]. In [3] authors point out that a
References
[1]
A. Jain, S. Pankanti, and R. Bolle, BIOMETRICS: Personal Identification in Networked Society, Kluwer, New York, NY, USA, 1999.
[2]
S. Ribaric, D. Ribaric, and N. Pavesic, “Multimodal biometric user-identification system for network-based applications,” IEE Proceedings on Vision, Image & Signal Processing, vol. 150, no. 6, pp. 409–416, 2003.
[3]
B. Schneier, “The uses and abuses of biometrics,” Communications of the ACM, vol. 42, no. 8, p. 136, 1999.
[4]
D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, Springer, 2003.
[5]
U. Uludag and A. K. Jain, “Attacks on biometric systems: a case study in fingerprints,” in Proceedings of the Security, Seganography and Watermarking of Multimedia Contents VI, vol. 5306 of Proceedings SPIE, pp. 622–633, San Jose, Calif, USA, January 2004.
[6]
N. K. Ratha, J. H. Connell, and R. M. Bolle:, “An analysis of minutiae matching strength,” in Proceedings of the 3rd International Conference on Audio- and Video-Based Biometric Person Authentication, pp. 223–228, June 2001.
[7]
V. Claus and S. Ralf, “Approaches to biometric watermarks for owner authentication,” in Proceedings of the Security and Watermarking of Multimedia Contents III, vol. 43 of Proceedings of SPIE, no. 14, pp. 209–219, January 2001.
[8]
A. K. Jain and U. Uludag, “Hiding fingerprint minutiae in images,” in Proceedings of 3rd Workshop on Automatic Identification Advanced Technologies (AutoID '02), pp. 97–102, 2002.
[9]
A. K. Jain and U. Uludag, “Hiding biometric data,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 11, pp. 1494–1498, 2003.
[10]
M. Vatsa, R. Singh, and A. Noore, “Improving biometric recognition accuracy and robustness using DWT and SVM watermarking,” IEICE Electronics Express, vol. 2, no. 12, pp. 362–367, 2005.
[11]
M. Vatsa, R. Singh, A. Noore, M. M. Houck, and K. Morris, “Robust biometric image watermarking for fingerprint and face template protection,” IEICE Electronics Express, vol. 3, no. 2, pp. 23–28, 2006.
[12]
N. K. Ratha, J. H. Connell, and R. M. Bolle, “Secure data hiding in wavelet compressed fingerprint images,” in International Multimedia Conference, Proceedings of the ACM Workshop on Multimedia, pp. 127–130, 2000.
[13]
A. K. Jain, U. Uludag, and R. L. Hsu, “Hiding a face in a fingerprint image,” Proceedings of International Conference on Pattern Recognition, vol. 3, pp. 756–759, 2002.
[14]
A. Noore, R. Singh, M. Vatsa, and M. M. Houck, “Enhancing security of fingerprints through contextual biometric watermarking,” Forensic Science International, vol. 169, no. 2-3, pp. 188–194, 2007.
[15]
N. Komninos and T. Dimitriou, “Protecting biometric templates with image watermarking technique,” in International Conference on Biometrics, vol. 4642 of Lecture Notes in Computer Science, pp. 114–123, 2007.
[16]
H. Al-Assam, H. Sellahewa, and S. Jassim, “A lightweight approach for biometric template protection,” in Proceedings of the Mobile Multimedia/Image Processing, Security, and Applications, vol. 7351 of Proceedings of SPIE, April 2009.
[17]
A. Nagar, K. Nandakumar, and A. K. Jain, “Biometric template transformation: a security analysis,” in The International Society for Optical Engineering, vol. 7541 of Proceedings of SPIE, January 2010.
[18]
M. Paunwala and S. Patnaik, “Robust biometric watermarking using image intrinsic local property for e-database applications,” in IASTED International Conference on Computer Vision, pp. 240–247, Vancouver, Canada, June 2011.
[19]
H. M. Patel, C. K. Modi, M. Paunwala, and S. Patnaik, “Human identification by partial iris segmentation using pupil circle growing based on binary Integrated edge intensity curve,” in Proceedings of the IEEE International Conference on Communication Systems and Network Technologies (CSNT '11), pp. 333–338, June 2011.
[20]
J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148–1161, 1993.
[21]
T. W. Ng, T. L. Tay, and S. W. Khor, “Iris recognition using rapid Haar wavelet decomposition,” in Proceedings of the 2nd International Conference on Signal Processing Systems (ICSPS '10), pp. V1820–V1823, July 2010.
[22]
J. Fridric, “Applications of data hiding in digital images,” in Tutorial for the International Symposium on Signal Processing and Its Applications (IPACS '98), Melbourne, Australia, November 1998.
[23]
Hiller and Lieberman, Introduction To Operations Research, Tata McGraw-Hill, 7th edition, 2001.
Y. Wang and A. Pearmain, “Blind image data hiding based on self reference,” Pattern Recognition Letters, vol. 25, no. 15, pp. 1681–1689, 2004.
[27]
Y. Choi and I. Aizawa, “Digital watermarking using inter block correlation,” in Proceedings of the International Conference on Image Processing, vol. 2, pp. 216–220, October 1999.
