Multi classifier-based score level fusion of multi-modal biometric recognition and its application to remote biometrics authentication

References

• Kadry, S. and Smaili, K. A design and implementation of a wireless IRIS recognition attendance management system. Inf. Technol. Control, 2007, 36/3, 323–329.
   Web of Science ®Google Scholar
• Veeramachaneni, K., Osadciw, L. A. and Varshney, P. K. Adaptive multimodal biometric fusion algorithm using particle swarm. Proc. SPIE, 2003, 5099, 211. doi: 10.1117/12.498270
   Google Scholar
• Camlikaya, E., Kholmatov, A. and Yanikoglu, B. Multi-biometric templates using fingerprint and voice. Biometric technology for human identification, No 5, 2008, Vol. 6944, Orlando FL, pp. 1–9.
   Google Scholar
• Porwik, P. and Wieclaw, L. A new efficient method of fingerprint image enhancement. Int. J. Biometrics, 2008, 1/1, 36–46. doi: 10.1504/IJBM.2008.018662
   Google Scholar
• Cheng, Y. and Larin, K. V. Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis. Appl. Optics, 2006, 45/36, 9238–9245. doi: 10.1364/AO.45.009238
   PubMed Web of Science ®Google Scholar
• Al-Osaimi, F. R. Spatially optimized data-level fusion of texture and shape for face recognition. IEEE Trans. Image Process., 2012, 21/2, 859–872. doi: 10.1109/TIP.2011.2165218
   PubMed Web of Science ®Google Scholar
• Paysan, P., Knothe, R., Amberg, B., Romdhani, S. and Vetter, T. Face recognition using 3-D models: pose and illumination. Proc. IEEE, 2009, 94/11, 1977–1999.
   Google Scholar
• Kekre, H. B. and ABharadi, V. Gabor filter based feature vector for dynamic signature recognition. Int. J. Comput. Appl., 2010, 2/3, 74–80, May.
   Google Scholar
• Lee, J.-C., Lee, C.-H., Hsu, C.-B., Kuei, P.-Y. and Chang, K.-C. Dorsal hand vein recognition based on 2D Gabor filters. Imaging Sci. J., 2014, 62/3, 127–138. doi: 10.1179/1743131X12Y.0000000049
   Web of Science ®Google Scholar
• Ross, A. and Jain, A. K. Information fusion in biometrics. Proc. of AVBPA, Halmstad, Sweden, June 2001, pp. 354–359.
   Google Scholar
• He, M., Horng, S.-J., Fan, P., Run, R.-S., Chen, R.-J., Lai, J.-L., Khan, M. K. and Sentosa, K. O. Performance evaluation of score level fusion in multimodal biometric systems. Pattern Recognit., May 2010, 43/5, 1789–1800. doi: 10.1016/j.patcog.2009.11.018
   Web of Science ®Google Scholar
• Lee, J-C., Su, Y., Tu, T-M. and Chang, C-P. A novel approach to image quality assessment in iris recognition systems. Imaging Sci. J., 2010, 58/3, 136–145. doi: 10.1179/136821909X12581187860059
   Web of Science ®Google Scholar
• Aravinth, J. and Valarmathy, S. Score-level fusion technique for multimodal biometric recognition using ABC-based neural network. Int. Rev. Comput. Software (IRECOS), August 2013, 8, 1889–1900.
   Google Scholar
• Yeh, H.-L., Chen, T.-H., Hu, K.-J. and Shih, W.-K. Robust elliptic curve cryptography-based three factor user authentication providing privacy of biometric data. IET Inf. Sec., 2013, 7/3, 247–252. doi: 10.1049/iet-ifs.2011.0348
   Web of Science ®Google Scholar
• Shekhar, S., Patel, V. M., Nasrabadi, N. M. and Chellappa, R. Joint sparse representation for robust multimodal biometrics recognition. IEEE Trans. Pattern Anal. Mach. Intell., 2014, 36/1, 113–126. doi: 10.1109/TPAMI.2013.109
   PubMed Web of Science ®Google Scholar
• Kikuchi, H., Nagai, K., Ogata, W. and Nishigaki, M. Privacy-preserving similarity evaluation and application to remote biometrics authentication. Soft Comput., 2010, 14, 529–536. doi: 10.1007/s00500-009-0449-6
   Web of Science ®Google Scholar
• Ali, H. S. and Abdalla, M. I. Score-level fusion for efficient multimodal person identification using face and speech. Int. J. Comput. Sci. Inf. Sec., 2013, 9/4, 48–53.
   Google Scholar
• Sapkal, S. Data level fusion for multi biometric system using face and finger. Int. J. Adv. Res. Comput. Sci. Electron. Eng., 2012, 1/2, 80–84.
   Google Scholar
• Wang, Y., Hu, J. and Han, F. Enhanced gradient-based algorithm for the estimation of fingerprint orientation fields. Appl. Math. Comput., 2007, 185, 823–833. doi: 10.1016/j.amc.2006.06.082
   Web of Science ®Google Scholar
• Bhattacharyya, D., Das, P., Bandyopadhyay, S. K. and Kim, T.-H. IRIS texture analysis and feature extraction for biometric pattern recognition. Int. J. Database Theory Appl., December 2008, 1/1, 53–60.
   Google Scholar
• Zhang, G. P. Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.), 2000, 30, 451–462. doi:10.1109/5326.897072
   Web of Science ®Google Scholar
• Patra, J. C. and Pal, R. N. A functional link artificial neural network for adaptive channel equalization. Signal Process., 1995, 43, 181–195. doi: 10.1016/0165-1684(94)00152-P
   Web of Science ®Google Scholar
• Dehuri, S. and Cho, S.-B. A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN. Neural Comput. Appl., 2010, 19, 187–205. doi: 10.1007/s00521-009-0288-5
   Web of Science ®Google Scholar
• Pham, D., et al. The bees algorithm, 2005 (Manufacturing Engineering Centre, Cardiff, Cardiff University, UK).
   Google Scholar
• Angelov, P. and Zhou, X. Evolving fuzzy-rule-based classifiers from data streams. IEEE Transact. Fuzzy Syst., 2008, 16/6, 1462–1475. doi: 10.1109/TFUZZ.2008.925904
   Web of Science ®Google Scholar
• John, G. H. and Langley, P. Estimating continuous distributions in Bayesian classifiers. Proc Eleventh Conf on ‘Uncertainty in Artificial Intelligence’, 1995, pp. 338–345.
   Google Scholar
• http://bias.csr.unibo.it/fvc2002/
   Google Scholar
• Chinese Academy of Sciences – Institute of Automation (CASIA), Database of 756 Gray scale Eye Images, http://www.cbsr.ia.ac.cn/IrisDatabase.htm,Version 1.0, 2005
   Google Scholar
• http://www.anefian.com/research/face_reco.html
   Google Scholar