Adapted fusion; Local fusion; Target-dependent fusion; User-dependent fusion
User-specific fusion in the framework of biometrics, initially devised for score fusion in the verification mode, refers to techniques used for information fusion in which there is a specific fusion function for each user enrolled in the system. These fusion functions are retrieved and used for information integration in the same way the enrolled templates corresponding to the claimed identities are retrieved and used for matching.
User-specific fusion techniques find application in several biometric fusion scenarios, e.g., multimodal fusion, where some subjects may be not adequate for recognition based on specific modalities (these evidences can be ignored or given less importance in the information fusion step), or multi-algorithm fusion, where some subjects may be better recognized based on particular algorithms (their fusion functions can be adapted to give more importance to those...
- 1.G. Doddington, W. Liggett, A. Martin, M. Przybocki, D. Reynolds, Sheeps, goats, lambs and wolves: a statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation, in Proceedings of International Conference on Speech and Language Processing, ICSLP, Sydney, 1998Google Scholar
- 2.J. Fierrez-Aguilar, J. Ortega-Garcia, J. Gonzalez-Rodriguez, Target dependent score normalization techniques and their application to signature verification. IEEE Trans. Syst. Man Cybern. Part C 35, 418–425 (2005)Google Scholar
- 3.N. Poh, J. Kittler, Incorporating model-specific score distribution in speaker verification systems. IEEE Trans. Audio Speech Lang. Process. 16, 594–606 (2008)Google Scholar
- 4.J. Fierrez-Aguilar, D. Garcia-Romero, J. Ortega-Garcia, J. Gonzalez-Rodriguez, Adapted user-dependent multimodal biometric authentication exploiting general information. Pattern Recognit. Lett. 26, 2628–2639 (2005)Google Scholar
- 5.A.K. Jain, A. Ross, Learning user-specific parameters in a multibiometric system, in Proceedings of IEEE International Conference on Image Processing, ICIP, Rochester, vol. 1, 2002, pp. 57–60Google Scholar
- 6.Y. Wang, Y. Wang, T. Tan, Combining fingerprint and voice biometrics for identity verification: an experimental comparison, in Proceedings of International Conference on Biometric Authentication, ICBA, Hong Kong, ed. by D. Zhang, A.K. Jain. LNCS, vol. 3072 (Springer, Berlin/Heidelberg, 2004), pp. 663–670Google Scholar
- 7.A. Kumar, D. Zhang, Integrating palmprint with face for user authentication, in Proceedings of Workshop on Multimodal User Authentication, MMUA, Santa Barbara, 2003, pp. 107–112Google Scholar
- 8.R. Snelick, U. Uludag, A. Mink, M. Indovina, A.K. Jain, Large scale evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE Trans. Pattern Anal. Mach. Intell. 27, 450–455 (2005)Google Scholar
- 9.N. Poh, S. Bengio, An investigation of f-ratio client-dependent normalisation on biometric authentication tasks, in, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, Philadelphia, vol. 1, 2005, pp. 721–724Google Scholar
- 10.K.A. Toh, X. Jiang, W.Y. Yau, Exploiting local and global decisions for multimodal biometrics verification. IEEE Trans. Signal Process. 52, 3059–3072 (2004)Google Scholar
- 11.J. Fierrez-Aguilar, D. Garcia-Romero, J. Ortega-Garcia, J. Gonzalez-Rodriguez, Bayesian adaptation for user-dependent multimodal biometric authentication. Pattern Recognit. 38, 1317–1319 (2005)Google Scholar
- 12.J. Fierrez-Aguilar, J. Ortega-Garcia, J. Gonzalez-Rodriguez, J. Bigun, Discriminative multimodal biometric authentication based on quality measures. Pattern Recognit. 38, 777–779 (2005)Google Scholar
- 13.J. Fierrez-Aguilar, D. Garcia-Romero, J. Ortega-Garcia, J. Gonzalez-Rodrigez, Speaker verification using adapted user-dependent multilevel fusion, in Proceedings of International Workshop on Multiple Classifier Systems, MCS, Seaside, ed. by N.C. Oza, R. Polikar, J. Kittler, F. Roli. LNCS, vol. 3541 (Springer, Berlin/Heidelberg, 2005), pp. 356–365Google Scholar