A Proposed Integrated Human Recognition for Security Reassurance
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| internalnotes | Aggarwal, S. and Y. Gulati, 2012. A multimodal biometric system using fingerprint and face. Int. J. Adv. Res. Comput. Eng. Technol., 1: 966-703. Ahmad, M.I., W.L. Woo and S.S. Dlay, 2010. Multimodal biometric fusion at feature level: Face and palmprint. Proceedings of the 7th International Symposium on Communication Systems Networks and Digital Signal Processing, Jul. 21-23, IEEE Xplore Press, Newcastle upon Tyne, pp: 801-805. Ahmad, T., A. Jameel and B. Ahmad, 2011. Pattern recognition using statistical and neural techniques. Proceedings of the International Conference on Computer Networks and Information Technology, Jul. 11-13, IEEE Xplore Press, Abbottabad, pp: 87-91. DOI: 10.1109/ICCNIT.2011.6020913 Asadi, S., D.V.S. Rao and V. Saikrishna, 2010. A comparative study of face recognition with principal component analysis and crosscorrelation technique. Int. J. Comput. Applic., 10: 17-21. DOI: 10.5120/1502-2019 Ashraf, A., M.Z. Walaa, M.S. Omar, M.N. Nadia and S. Gerald, 2010. Human authentication using faee and fingerprint biometries. Proceedings of the 2nd International Conference on Computational Intelligence, Communication Systems and Networks, (CSN’ 10), pp: 274-278. Belhumeur, P.N., J.P. Hespanha and D. Kriegman, 1997. Eigenfaces Vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Analysis Machine Itellig., 19: 711-720. DOI: 10.1109/34.598228 Bhattacharyya, D., R. Ranjan, A.F. Alisherov and M. Choi, 2009. Biometrie authentieation: A review. Int. J. u-and e-Service, Sci. Technol., 2: 13-27. BWG, 2009. Biometrics Security Concerns. UK Biometrics Working Group. Chaudhary, S. and R. Nath, 2009. A multimodal biometric recognition system based on fusion of palmprint, fingerprint and face. Proceedings of the International Conference on Advances in Recent Technologies in Communication and Computing, Oct. 27-28, IEEE Xplore Press, Kottayam, Kerala, pp: 596-600. DOI: 10.1109/ARTCom.2009.224 Conti, V., C. Militello and F. Sorbello, 2010. A frequency-based approach for features fusion in fingerprint and iris multimodal biometric identification systems. IEEE Trans. Syst. Man, Cybernetics-Part C: Applic. Rev., 40: 384-395. DOI: 10.1109/TSMCC.2010.2045374 Deriche, M., 2008. Trends and challenges in mono and multi biometrics. Proceeding of the 1st Workshops on Image Processing Theory, Tools and Applications, Nov. 23-26, IEEE Xplore Press, Sousse, pp: 1-9. DOI: 10.1109/IPTA.2008.4743801 Deshmukh, A., S. Pawar and M. Joshi, 2013. Feature level fusion of face and fingerprint modalities using Gabor filter bank. Proceedings of the International Conference on Signal Processing, Computing and Control, Sept. 26-28, IEEE Xplore Press, Solan, pp: 1-5. DOI: 10.1109/ISPCC.2013.6663404 Dinakardas, C., S.P. Sankar and G. Nisha, 2013. A multimodal performance evaluation on two different models based on face, fingerprint and iris templates. Proceedings of the International Conference on Emerging Trends in VLSI, Embedded System, Nano Electronics and Telecommunication System, Jan. 7- 9, IEEE Xplore Press, Tiruvannamalai, pp: 1-6. DOI: 10.1109/ICEVENT.2013.6496558 Faten, B., B. Mossaad and L. Kais, 2013. Multimodal biometric identification system based on face and fingerprint. Proc. Eng. Technol., 3: 219-222. Ghandehari, A. and R. Safabakhsh, 2011. A comparison of principal component analysis and adaptive principal component extraction for palmprint recognition. Proceedings of the International Conference on Hand-Based Biometrics, Nov. 17-18, IEEE Xplore Press, Hong Kong, pp: 1-6. DOI: 10.1109/ICHB.2011.6094307 Hanmandlu, M., J. Grover, V.K. Madasu and S. Vasirkala, 2010. Score level fusion of hand based biometrics using t-norms. Proceedings of the International Conference on Technologies for Homeland Security, Nov. 8-10, IEEE Xplore Press, Waltham, MA, pp: 70-76. DOI: 10.1109/THS.2010.5655093 Hanuma, M., 2011. Real-time live face detection using face template matching and DCT energy analysis. Proceedings of the International Conference of Soft Computing and Pattern Recognition (CPR’ 11), pp: 342-346. He, M., S.J. Horng, P. Fanc, R.S. Rund and R.J. Chend et al., 2010. Performance evaluation of score level fusion in multimodal biometric systems. Pattern Recognit., 43: 1789-1800. Imran, M., A. Rao and G.H. Kumar, 2010. Multibiometric systems: A comparative study of multi-algorithmic and multimodal approaches. Procedia Comput. Sci., 2: 207-212. DOI: 10.1016/j.procs.2010.11.026 Imran, M., A. Rao and G.H. Kumar, 2011. A new hybrid approach for information fusion in multibiometric systems. Proceedings of the 3rd National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, Dec. 15-17, IEEE Xplore Press, Hubli, Karnataka, pp: 235-238. DOI: 10.1109/NCVPRIPG.2011.57 Imran, M., A. Rao and G.H. Kumar, 2013. Multimodal biometric fusion of face and palmprint at various levels. Proceedings of the International Conference on Advances in Computing, Communications and Informatics, Aug. 22-25, IEEE Xplore Press, Mysore, pp: 1793-1798. DOI: 10.1109/ICACCI.2013.6637453 Jacey-Lynn, M. and D. Gillies, 2011. A tensor-based multivariate statistical model for 3D face and facial expression recognition. Proceedings of the 7th International Conference on Information Technology in Asia, Jul. 12-13, IEEE Xplore Press, Kuching, Sarawak, pp: 1-8. DOI: 10.1109/CITA.2011.5998383 Jain, A.K. and R. Arun, 2007. Handbook of Multimodal biometrics. Springer. Jain, A.K., A. Ross and S. Prabhakar, 2004. An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol., 14: 4-20. DOI: 10.1109/TCSVT.2003.818349 Jiaqiang, W., Y. Ming, Q. Hanbing and L. Bin, 2013. Analysis of palm vein image quality and recognition with different distance. Proceedings of the Fourth International Conference on Digital Manufacturing and Automation Jun. 29-30, IEEE Xplore Press, Qingdao, pp: 215-218. DOI: 10.1109/ICDMA.2013.50 Kaur, G., A. Girdhar and M. Kaur, 2010. Enhanced iris recognition system-an integrated approach to person identification. Int. J. Comput. Applic., 8: 1-5. DOI: 10.5120/1182-1630 Kittler, J. and S.A. Hojjatoleslami, 1998. A weighted combination of classifiers employing shared and distinct representations. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Jun. 23-25, IEEE Xplore Press, Santa Barbara, CA, pp: 924-929. DOI: 10.1109/CVPR.1998.698715 Ko, T., 2005. Multimodal biometric identification for large user population using fingerprint, face and iris recognition. Proceedings of the 34th Applied Imagery and Pattern Recognition Workshop, Dec. 1-1, IEEE Xplore Press, Washington, DC, pp: 223-228. DOI: 10.1109/AIPR.2005.35 Mohammed, J. and B. Gupta, 2013. Performance comparison of various face detection techniques. Int. J. Sci. Res. Eng. Technol., 2: 46-52. Nandakumar, K., Y. Chen, S.C. Dass and A. Jain, 2008. Likelihood ratio-based biometric score fusion. IEEE Trans. Pattern Analysis Mach. Intellig., 30: 342-347. DOI: 10.1109/TPAMI.2007.70796. NIST, 2002. Summary of NIST standards for biometric accuracy, tamper resista NCE and interoperability. Önsen, T.A.A., 2003. Face recognition using PCA, LDA and ICA approaches on colored images. J. Electrical Electron. Eng. Park, Y.H., D.N. Tien, E.C. Lee, K.R. Park and H.C. Kim, 2011. A multimodal biometric recognition of touched fingerprint and finger-vein. Proceedings of the International Conference on Multimedia and Signal Processing, May 14-15, IEEE Xplore Press, Guilin, Guangxi, pp: 247-250. DOI: 10.1109/CMSP.2011.57 Ravi, S. and P. Dattatreya, 2013. Multimodal biometric approach using fingerprint, face and enhanced iris features recognition. Proceedings of the International Conference on Circuits, Power and Computing Technologies, Mar. 20-21, IEEE Xplore Press, Nagercoil, pp: 1143-1150. DOI: 10.1109/ICCPCT.2013.6528884 Ross, A. and A. Jain, 2003. Information fusion in biometrics. Pattern Recogn. Lett., 24: 2115-2125. DOI: 10.1016/S0167–8655(03)00079-5 Ross, A.A., K. Nandakumar and A.K. Jain, 2006. Handbook of Multibiometrics. 1st Edn., Springer Science and Business Media, New York, ISBN-10: 0387331239, pp: 220. Samadi, A. and H. Pourghassem, 2013. Children detection algorithm based on statistical models and LDA in human face images. Proceedings of the International Conference on Communication Systems and Network Technologies, Apr. 6-8, IEEE Xplore Press, Gwalior, pp: 206-209. DOI: 10.1109/CSNT.2013.52 Sangram, B. and K. Davinder, 2011. Fingerprint recognition using image segmentation. Int. J. Adv. Eng. Sci. Technol., 5: 012-023. Soviany, S. and S. Puscoci, 2013. A feature correlationbased fusion method for fingerprint and palmprint identification systems. Proceedings of the 4th International Conference on E-Health and Bioengineering-EHB, Nov. 21-23, IEEE Xplore Press, Iasi, 1-4. DOI: 10.1109/EHB.2013.6707259 Swets, D.L. and J.J. Weng, 1996. Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Analysis Mach. Intellig., 18: 831-836. DOI: 10.1109/34.531802 Tekade, A.A., 2012. Feature fusion method based on fisher discriminant analysis for face and ear for multimodal recognition. Int. J. Eng. Res. Technol. Vivek, S.A., J. Aravinth and S. Valarmathy, 2012. Feature extraction for multimodal biometric and study of fusion using Gaussian mixture model. Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering, Mar. 21-23, IEEE Xplore Press, Salem, Tamilnadu, pp: 387-392. DOI: 10.1109/ICPRIME.2012.6208377 Wang, J.G., W.Y. Yau and A. Suwandy, 2007. Fusion of palmprint and palm vein images for person recognition based on “Laplacianpalm” feature. Proceedings of the Conference on Computer Vision and Pattern Recognition, Jun. 17-22, IEEE Xplore Press, Minneapolis, MN, pp: 1-8. DOI: 10.1109/CVPR.2007.383386 Wang, Z., C. Liu, T. Shi and Q. Ding, 2013. Face-palm identification system on feature level fusion based on CCA. J. Inform. Hiding Multimedia Signal Process., 4: 272-279. Xiuyan, L., M. Changyun L. Tiegen and Y. Chenhu, 2011. Theoretical analysis and experimental study on multimodal biometric. Proceedings of the International Conference on Control, Automation and Systems Engineering, Jul. 30-31, IEEE Xplore Press, Singapore, 1-4. DOI: 10.1109/ICCASE.2011.5997781 Xu, Y., F. Luo, Y. Xu and Y. Zhai, 2013. Multi-modal biometric recognition algorithm based on iris and facial images. J. Comput. Inform. Syst., 9: 6743-6750. Yaghoubi, Z. and M. Eliasi, 2011. Robust biometric authentication based on feature extracted from visual ventral stream. Proceedings of the IEEE International Conference on Computer Applications and Industrial Electronics, Dec. 4-7, IEEE Xplore Press, Penang, pp: 448-452. DOI: 10.1109/ICCAIE.2011.6162177 Yang, F. and B. Ma, 2007. A new mixed-mode biometrics information fusion based-on fingerprint, hand-geometry and palm-print. Proceedings of the 4th International Conference on Image and Graphics, Aug. 22-24, IEEE Xplore Press, Sichuan, pp: 689-693. DOI: 10.1109/ICIG.2007.39 Yang, F., B. Ma, Q.X. Wang and D. Yao and F. Chenyan, 2007. Information fusion of biometrics based-on fingerprint, Hand-geometry and Palmprint. Proceedings of the Workshop on Automatic Identification Advanced Technologies, Jun. 7-8, IEEE Xplore Press, Alghero, pp: 247-252. DOI: 10.1109/AUTOID.2007.380628 Yanxia, W. and R. Qiuqi, 2006. Kernel fisher discriminant analysis for palmprint recognition. Proceedings of the 18th International Conference on Pattern Recognition, IEEE Xplore Press, Hong Kong, pp: 457-460. DOI: 10.1109/ICPR.2006.737 Yazdanpanah, A.P., K. Faez and R. Amirfattahi, 2010. Multimodal biometric system using face, ear and gait biometrics. Proceedings of the Information Sciences Signal Processing and their Applications, May 10-13, IEEE Xplore Press, Kuala Lumpur, pp: 251-254. DOI: 10.1109/ISSPA.2010.5605477 Zhonghua, L. and L. Bibo, 2010. Iris recognition method based on the coefficients of morlet wavelet transform. Proceedings of the International Conference on Intelligent Computation Technology and Automation, May 11-12, IEEE Xplore Press, Changsha, pp: 576-580. DOI: 10.1109/ICICTA.2010.783 |
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| spelling | 11903 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=11903 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf Adobe Acrobat Pro DC 20 Paper Capture Plug-in with ClearScan 12 1.6 User user USER UsEr 2024-08-26 17:37:09 6204-01-FH02-FIK-15-03326.pdf UniSZA Private Access A Proposed Integrated Human Recognition for Security Reassurance American Journal of Applied Sciences A lot of systems require reliable and thorough authentication to ratify individual’s claimed identity, especially in national and international security and defense. Unibiometric system suffers inherent weaknesses that are unavoidable in the system. Such inadequacies may directly or indirectly lead to an unacceptable error. For this reason, researchers pay great attention to the more advanced biometrics (referred to as multimodal biometrics). A multimodal biometric system involves at least two unimodal traits in a sole identification. This alleviates some of the drawbacks and improves recognition accuracy despite number of population considered. This research proclaims a new technique of integration for human recognition improvement using four physiological characteristics: Face, iris, palmprint and thumbprint. In addition, two fusion strategies namely, score-level and decision-level are presented using robust algorithms. The multimodal biometric systems possessed relative merits or pluses over its counterpart monomodal biometrics. The higher biometric modalities are integrated the more system is secured, hence better security assurance. Therefore, successfully conducting our research can derive supreme benefit over conventional multimodal biometrics. The integration results to outstanding gap coverage for security reassurance. 12 2 Science Publications Science Publications 155-165 Aggarwal, S. and Y. Gulati, 2012. A multimodal biometric system using fingerprint and face. Int. J. Adv. Res. Comput. Eng. Technol., 1: 966-703. Ahmad, M.I., W.L. Woo and S.S. Dlay, 2010. Multimodal biometric fusion at feature level: Face and palmprint. Proceedings of the 7th International Symposium on Communication Systems Networks and Digital Signal Processing, Jul. 21-23, IEEE Xplore Press, Newcastle upon Tyne, pp: 801-805. Ahmad, T., A. Jameel and B. Ahmad, 2011. Pattern recognition using statistical and neural techniques. Proceedings of the International Conference on Computer Networks and Information Technology, Jul. 11-13, IEEE Xplore Press, Abbottabad, pp: 87-91. DOI: 10.1109/ICCNIT.2011.6020913 Asadi, S., D.V.S. Rao and V. Saikrishna, 2010. A comparative study of face recognition with principal component analysis and crosscorrelation technique. Int. J. Comput. Applic., 10: 17-21. DOI: 10.5120/1502-2019 Ashraf, A., M.Z. Walaa, M.S. Omar, M.N. Nadia and S. Gerald, 2010. Human authentication using faee and fingerprint biometries. Proceedings of the 2nd International Conference on Computational Intelligence, Communication Systems and Networks, (CSN’ 10), pp: 274-278. Belhumeur, P.N., J.P. Hespanha and D. Kriegman, 1997. Eigenfaces Vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Analysis Machine Itellig., 19: 711-720. DOI: 10.1109/34.598228 Bhattacharyya, D., R. Ranjan, A.F. Alisherov and M. Choi, 2009. Biometrie authentieation: A review. Int. J. u-and e-Service, Sci. Technol., 2: 13-27. BWG, 2009. Biometrics Security Concerns. UK Biometrics Working Group. Chaudhary, S. and R. Nath, 2009. A multimodal biometric recognition system based on fusion of palmprint, fingerprint and face. Proceedings of the International Conference on Advances in Recent Technologies in Communication and Computing, Oct. 27-28, IEEE Xplore Press, Kottayam, Kerala, pp: 596-600. DOI: 10.1109/ARTCom.2009.224 Conti, V., C. Militello and F. Sorbello, 2010. A frequency-based approach for features fusion in fingerprint and iris multimodal biometric identification systems. IEEE Trans. Syst. Man, Cybernetics-Part C: Applic. Rev., 40: 384-395. DOI: 10.1109/TSMCC.2010.2045374 Deriche, M., 2008. Trends and challenges in mono and multi biometrics. Proceeding of the 1st Workshops on Image Processing Theory, Tools and Applications, Nov. 23-26, IEEE Xplore Press, Sousse, pp: 1-9. DOI: 10.1109/IPTA.2008.4743801 Deshmukh, A., S. Pawar and M. Joshi, 2013. Feature level fusion of face and fingerprint modalities using Gabor filter bank. Proceedings of the International Conference on Signal Processing, Computing and Control, Sept. 26-28, IEEE Xplore Press, Solan, pp: 1-5. DOI: 10.1109/ISPCC.2013.6663404 Dinakardas, C., S.P. Sankar and G. Nisha, 2013. A multimodal performance evaluation on two different models based on face, fingerprint and iris templates. Proceedings of the International Conference on Emerging Trends in VLSI, Embedded System, Nano Electronics and Telecommunication System, Jan. 7- 9, IEEE Xplore Press, Tiruvannamalai, pp: 1-6. DOI: 10.1109/ICEVENT.2013.6496558 Faten, B., B. Mossaad and L. Kais, 2013. Multimodal biometric identification system based on face and fingerprint. Proc. Eng. Technol., 3: 219-222. Ghandehari, A. and R. Safabakhsh, 2011. A comparison of principal component analysis and adaptive principal component extraction for palmprint recognition. Proceedings of the International Conference on Hand-Based Biometrics, Nov. 17-18, IEEE Xplore Press, Hong Kong, pp: 1-6. DOI: 10.1109/ICHB.2011.6094307 Hanmandlu, M., J. Grover, V.K. Madasu and S. Vasirkala, 2010. Score level fusion of hand based biometrics using t-norms. Proceedings of the International Conference on Technologies for Homeland Security, Nov. 8-10, IEEE Xplore Press, Waltham, MA, pp: 70-76. DOI: 10.1109/THS.2010.5655093 Hanuma, M., 2011. Real-time live face detection using face template matching and DCT energy analysis. Proceedings of the International Conference of Soft Computing and Pattern Recognition (CPR’ 11), pp: 342-346. He, M., S.J. Horng, P. Fanc, R.S. Rund and R.J. Chend et al., 2010. Performance evaluation of score level fusion in multimodal biometric systems. Pattern Recognit., 43: 1789-1800. Imran, M., A. Rao and G.H. Kumar, 2010. Multibiometric systems: A comparative study of multi-algorithmic and multimodal approaches. Procedia Comput. Sci., 2: 207-212. DOI: 10.1016/j.procs.2010.11.026 Imran, M., A. Rao and G.H. Kumar, 2011. A new hybrid approach for information fusion in multibiometric systems. Proceedings of the 3rd National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, Dec. 15-17, IEEE Xplore Press, Hubli, Karnataka, pp: 235-238. DOI: 10.1109/NCVPRIPG.2011.57 Imran, M., A. Rao and G.H. Kumar, 2013. Multimodal biometric fusion of face and palmprint at various levels. Proceedings of the International Conference on Advances in Computing, Communications and Informatics, Aug. 22-25, IEEE Xplore Press, Mysore, pp: 1793-1798. DOI: 10.1109/ICACCI.2013.6637453 Jacey-Lynn, M. and D. Gillies, 2011. A tensor-based multivariate statistical model for 3D face and facial expression recognition. Proceedings of the 7th International Conference on Information Technology in Asia, Jul. 12-13, IEEE Xplore Press, Kuching, Sarawak, pp: 1-8. DOI: 10.1109/CITA.2011.5998383 Jain, A.K. and R. Arun, 2007. Handbook of Multimodal biometrics. Springer. Jain, A.K., A. Ross and S. Prabhakar, 2004. An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol., 14: 4-20. DOI: 10.1109/TCSVT.2003.818349 Jiaqiang, W., Y. Ming, Q. Hanbing and L. Bin, 2013. Analysis of palm vein image quality and recognition with different distance. Proceedings of the Fourth International Conference on Digital Manufacturing and Automation Jun. 29-30, IEEE Xplore Press, Qingdao, pp: 215-218. DOI: 10.1109/ICDMA.2013.50 Kaur, G., A. Girdhar and M. Kaur, 2010. Enhanced iris recognition system-an integrated approach to person identification. Int. J. Comput. Applic., 8: 1-5. DOI: 10.5120/1182-1630 Kittler, J. and S.A. Hojjatoleslami, 1998. A weighted combination of classifiers employing shared and distinct representations. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Jun. 23-25, IEEE Xplore Press, Santa Barbara, CA, pp: 924-929. DOI: 10.1109/CVPR.1998.698715 Ko, T., 2005. Multimodal biometric identification for large user population using fingerprint, face and iris recognition. Proceedings of the 34th Applied Imagery and Pattern Recognition Workshop, Dec. 1-1, IEEE Xplore Press, Washington, DC, pp: 223-228. DOI: 10.1109/AIPR.2005.35 Mohammed, J. and B. Gupta, 2013. Performance comparison of various face detection techniques. Int. J. Sci. Res. Eng. Technol., 2: 46-52. Nandakumar, K., Y. Chen, S.C. Dass and A. Jain, 2008. Likelihood ratio-based biometric score fusion. IEEE Trans. Pattern Analysis Mach. Intellig., 30: 342-347. DOI: 10.1109/TPAMI.2007.70796. NIST, 2002. Summary of NIST standards for biometric accuracy, tamper resista NCE and interoperability. Önsen, T.A.A., 2003. Face recognition using PCA, LDA and ICA approaches on colored images. J. Electrical Electron. Eng. Park, Y.H., D.N. Tien, E.C. Lee, K.R. Park and H.C. Kim, 2011. A multimodal biometric recognition of touched fingerprint and finger-vein. Proceedings of the International Conference on Multimedia and Signal Processing, May 14-15, IEEE Xplore Press, Guilin, Guangxi, pp: 247-250. DOI: 10.1109/CMSP.2011.57 Ravi, S. and P. Dattatreya, 2013. Multimodal biometric approach using fingerprint, face and enhanced iris features recognition. Proceedings of the International Conference on Circuits, Power and Computing Technologies, Mar. 20-21, IEEE Xplore Press, Nagercoil, pp: 1143-1150. DOI: 10.1109/ICCPCT.2013.6528884 Ross, A. and A. Jain, 2003. Information fusion in biometrics. Pattern Recogn. Lett., 24: 2115-2125. DOI: 10.1016/S0167–8655(03)00079-5 Ross, A.A., K. Nandakumar and A.K. Jain, 2006. Handbook of Multibiometrics. 1st Edn., Springer Science and Business Media, New York, ISBN-10: 0387331239, pp: 220. Samadi, A. and H. Pourghassem, 2013. Children detection algorithm based on statistical models and LDA in human face images. Proceedings of the International Conference on Communication Systems and Network Technologies, Apr. 6-8, IEEE Xplore Press, Gwalior, pp: 206-209. DOI: 10.1109/CSNT.2013.52 Sangram, B. and K. Davinder, 2011. Fingerprint recognition using image segmentation. Int. J. Adv. Eng. Sci. Technol., 5: 012-023. Soviany, S. and S. Puscoci, 2013. A feature correlationbased fusion method for fingerprint and palmprint identification systems. Proceedings of the 4th International Conference on E-Health and Bioengineering-EHB, Nov. 21-23, IEEE Xplore Press, Iasi, 1-4. DOI: 10.1109/EHB.2013.6707259 Swets, D.L. and J.J. Weng, 1996. Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Analysis Mach. Intellig., 18: 831-836. DOI: 10.1109/34.531802 Tekade, A.A., 2012. Feature fusion method based on fisher discriminant analysis for face and ear for multimodal recognition. Int. J. Eng. Res. Technol. Vivek, S.A., J. Aravinth and S. Valarmathy, 2012. Feature extraction for multimodal biometric and study of fusion using Gaussian mixture model. Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering, Mar. 21-23, IEEE Xplore Press, Salem, Tamilnadu, pp: 387-392. DOI: 10.1109/ICPRIME.2012.6208377 Wang, J.G., W.Y. Yau and A. Suwandy, 2007. Fusion of palmprint and palm vein images for person recognition based on “Laplacianpalm” feature. Proceedings of the Conference on Computer Vision and Pattern Recognition, Jun. 17-22, IEEE Xplore Press, Minneapolis, MN, pp: 1-8. DOI: 10.1109/CVPR.2007.383386 Wang, Z., C. Liu, T. Shi and Q. Ding, 2013. Face-palm identification system on feature level fusion based on CCA. J. Inform. Hiding Multimedia Signal Process., 4: 272-279. Xiuyan, L., M. Changyun L. Tiegen and Y. Chenhu, 2011. Theoretical analysis and experimental study on multimodal biometric. Proceedings of the International Conference on Control, Automation and Systems Engineering, Jul. 30-31, IEEE Xplore Press, Singapore, 1-4. DOI: 10.1109/ICCASE.2011.5997781 Xu, Y., F. Luo, Y. Xu and Y. Zhai, 2013. Multi-modal biometric recognition algorithm based on iris and facial images. J. Comput. Inform. Syst., 9: 6743-6750. Yaghoubi, Z. and M. Eliasi, 2011. Robust biometric authentication based on feature extracted from visual ventral stream. Proceedings of the IEEE International Conference on Computer Applications and Industrial Electronics, Dec. 4-7, IEEE Xplore Press, Penang, pp: 448-452. DOI: 10.1109/ICCAIE.2011.6162177 Yang, F. and B. Ma, 2007. A new mixed-mode biometrics information fusion based-on fingerprint, hand-geometry and palm-print. Proceedings of the 4th International Conference on Image and Graphics, Aug. 22-24, IEEE Xplore Press, Sichuan, pp: 689-693. DOI: 10.1109/ICIG.2007.39 Yang, F., B. Ma, Q.X. Wang and D. Yao and F. Chenyan, 2007. Information fusion of biometrics based-on fingerprint, Hand-geometry and Palmprint. Proceedings of the Workshop on Automatic Identification Advanced Technologies, Jun. 7-8, IEEE Xplore Press, Alghero, pp: 247-252. DOI: 10.1109/AUTOID.2007.380628 Yanxia, W. and R. Qiuqi, 2006. Kernel fisher discriminant analysis for palmprint recognition. Proceedings of the 18th International Conference on Pattern Recognition, IEEE Xplore Press, Hong Kong, pp: 457-460. DOI: 10.1109/ICPR.2006.737 Yazdanpanah, A.P., K. Faez and R. Amirfattahi, 2010. Multimodal biometric system using face, ear and gait biometrics. Proceedings of the Information Sciences Signal Processing and their Applications, May 10-13, IEEE Xplore Press, Kuala Lumpur, pp: 251-254. DOI: 10.1109/ISSPA.2010.5605477 Zhonghua, L. and L. Bibo, 2010. Iris recognition method based on the coefficients of morlet wavelet transform. Proceedings of the International Conference on Intelligent Computation Technology and Automation, May 11-12, IEEE Xplore Press, Changsha, pp: 576-580. DOI: 10.1109/ICICTA.2010.783 |
| spellingShingle | A Proposed Integrated Human Recognition for Security Reassurance |
| summary | A lot of systems require reliable and thorough authentication to ratify individual’s claimed identity, especially in national and international security and defense. Unibiometric system suffers inherent weaknesses that are unavoidable in the system. Such inadequacies may directly or indirectly lead to an unacceptable error. For this reason, researchers pay great attention to the more advanced biometrics (referred to as multimodal biometrics). A multimodal biometric system involves at least two unimodal traits in a sole identification. This alleviates some of the drawbacks and improves recognition accuracy despite number of population considered. This research proclaims a new technique of integration for human recognition improvement using four physiological characteristics: Face, iris, palmprint and thumbprint. In addition, two fusion strategies namely, score-level and decision-level are presented using robust algorithms. The multimodal biometric systems possessed relative merits or pluses over its counterpart monomodal biometrics. The higher biometric modalities are integrated the more system is secured, hence better security assurance. Therefore, successfully conducting our research can derive supreme benefit over conventional multimodal biometrics. The integration results to outstanding gap coverage for security reassurance. |
| title | A Proposed Integrated Human Recognition for Security Reassurance |
| title_full | A Proposed Integrated Human Recognition for Security Reassurance |
| title_fullStr | A Proposed Integrated Human Recognition for Security Reassurance |
| title_full_unstemmed | A Proposed Integrated Human Recognition for Security Reassurance |
| title_short | A Proposed Integrated Human Recognition for Security Reassurance |
| title_sort | proposed integrated human recognition for security reassurance |