Biometric is the technique for the recognition of the physiological and biological features which are the face, iris, and fingerprint and pattern analysis. Biometric is the method of identifying the biometric features. Some issues in the uni-modal biometric scheme that reduced performance and accuracy. To overcome the effect in the uni-modal biometric, biometric fusion can be used through a multimodal biometric system. Biometric fusion is a method of using multiple biometric information and steps for the processing of the information to improve the biometric system. Multi-model biometric systems meet various security issues and sometimes un-acceptance false rejection errors, false rejection rate, and error rates. Some of these problems can be reduced by setting up multi-model biometric systems. It supports joining twice biometric traits in a verification system to enhance the accuracy rate and Specificity. However, features of different biometrics have to be independent. In this research work, proposes a multi-modal biometric recognized using fingerprint and speech recognition. In the proposed approach, a Novel, user authentication system, based on a combined acquisition of Fingerprint and Speech with high accuracy rate, Precision, false acceptance rate, and false rejection rate. In fingerprint using Minutiae method and Speech using MFCC method used for feature extraction method. In this research work, develop a project application in MATLAB 2016a simulation tool and has developed a score level fusion of various or multiple biometrics help to reduce the system error rates.

Score Level Fusion, Multi-model Biometric System, Minutiae, and MFCC feature Extraction

[1] Ulery, B., Hicklin, A., Watson, C., Fellner, W., & Hallinan, P. (2006). Studies of biometric fusion. NIST Interagency Report, 7346.
[2] Jiang, Y., & Liu, X. (2018). Uniform Local Binary Pattern for Fingerprint Liveness Detection in the Gaussian Pyramid. Journal of Electrical and Computer Engineering, 2018.
[3] Mwaura, G. W., Mwangi, W., & Otieno, C. (2017). Multimodal Biometric System:-Fusion Of Face And Fingerprint Biometrics At Match Score Fusion Level. International Journal of Scientific & Technology Research, 6(4), 41-49..
[4] Phillips, P. J., McCabe, R. M., & Chellappa, R. (1998, September). Biometric image processing and recognition. In 9th European Signal Processing Conference (EUSIPCO 1998)(pp. 1-8). IEEE.
[5] Jain, A. K., Prabhakar, S., & Ross, A. (1998). Biometrics-based web access. Transactions of the Institute of British Geographers, ns, 7, 458-473.
[6] Podio, F. L. (2001). Biometrics—technologies for highly secure personal authentication. National Institute of Standards and Technology, http://whitepapers. , . com/search. aspx.
[7] Jain, A., & Pankanti, S. (2001). Biometrics systems: anatomy of performance. IEICE transactions on information and systems, 84(7), 788-799.
[8] Hopkins, R. (1999). An introduction to biometrics and large scale civilian identification. International Review of Law, Computers & Technology, 13(3), 337-363.
[9] Bhatnagar, N., Sinha, A., Samdaria, N., Gupta, A., Batra, S., Bhardwaj, M., & Thies, W. (2012, June). Biometric monitoring as a persuasive technology: ensuring patients visit health centers in India’s slums. In International Conference on Persuasive Technology (pp. 169-180). Springer, Berlin, Heidelberg.
[10] Jain, A. K., Ross, A., & Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Transactions on circuits and systems for video technology, 14(1).
[11] Shubhangi, D. C., & Bali, M. (2012). Multi-biometric approaches to face and fingerprint biometrics. International Journal of Engineering Research & Technology, 1(5), 2278-0181.
[12] Singh, M., & Gupta, S. (2012). Correlation studies of PPG finger pulse profiles for Biometric system. Int. J. Inf. Technol. Knowl. Manage, 5, 1-3.
[13] Shunmugam, S., & Selvakumar, R. K. (2014, December). Electronic transaction authentication—A survey on multimodal biometrics. In 2014 IEEE International Conference on Computational Intelligence and Computing Research (pp. 1-4). IEEE.
[14] Garje, P. D., & Agrawal, S. S. (2012). Multibiometric identification system based on score level fusion. IOSR Journal of Electronics and Communication Engineering (IOSRJECE), 2(6), 07-11.
[15] Ghayoumi, M. (2015, June). A review of multimodal biometric systems: Fusion methods and their applications. In 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS) (pp. 131-136). IEEE.
[16] Bashar, K. (2018, October). ECG and EEG Based Multimodal Biometrics for Human Identification. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 4345-4350). IEEE.
[17] Dehzangi, O., & Williams, C. (2015, June). Towards multi-modal wearable driver monitoring: Impact of road condition on driver distraction. In 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)(pp. 1-6). IEEE.
[18] Evangelin, L. N., & Fred, A. L. (2017, March). Feature level fusion approach for personal authentication in multimodal biometrics. In 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM) (pp. 148-151). IEEE.
[19] Shunmugam, S., & Selvakumar, R. K. (2014, December). Electronic transaction authentication—A survey on multimodal biometrics. In 2014 IEEE International Conference on Computational Intelligence and Computing Research (pp. 1-4). IEEE.
[20] Biometrics.idealtest.org, 2019. [Online]. Available: http://biometrics.idealtest.org. [Accessed: 28- May- 2019].
[21] Marsyas.info, 2019. [Online]. Available: http://marsyas.info/downloads/datasets.html. [Accessed: 28- May- 2019].