A biometric is a unique feature, a measurable trait or characteristic which is utilized in automatically identifying or verifying the identity of a human being. An assortment of inevitable shortcomings has been faced by unimodal biometric recognition like Limited discriminability, noisy biometric data, Upper bound in performance and Lack of permanence, consequence dilapidation of exactness and performance of the system. Multimodal biometrics consolidates the two or more biometric features into a single detection. Problems transpired in unimodal recognition can be alleviated by using multimodal biometric systems that fuse evidence from scores of multiple biometric systems and characteristically provide better recognition as compared to unimodal biometric systems. Biometric authentications exploit inimitable combination of measurable physical Characteristics- fingerprint, finger vein features, voice print, iris of the eye, and so on- that cannot be willingly imitated or forged by others. This paper proffers the match score level of fusion with feature extraction that can be espoused to consolidate the scores attained by fingerprint and finger-vein and new technique fusion with alignment that are credible and the integration strategic that can be espoused to overlapped the features attained by fingerprint and vein. Fusion techniques include processing biometric modalities successively until an adequate match is obtained.

1. Miura, N. , Nagasaka, A. and Miyatake, T. 2004. Feature extraction of finger vein patterns based on repeated line tracking and its application to personal identification. 194–203.
2. Kaur, M. ,Singh, M. ,Girdhar, A. and Sandhu,P. S. 2008. Fingerprint Verification System using Minutiae Extraction Technique. World Academy of Science, Engineering and Technology . 497-502 .
3. Feifei, C. and Gongping, Y. 2011. Score Level Fusion of Fingerprint and Finger Vein Recognition. Journal of Information Systems . 5723-5731.
4. Kang, B. J. andPark,K. R. 2010. Multimodal biometric authentication based on the fusion of vein and geometry of single finger. The Institution of Engineering and Technology . 209-217. .
5. Aboalsamh,A. H. 2011. a multi biometric system using combined vein and fingerprint identification. international journal of circuits, systems and signal processing. 29-36 .
6. Garje, P. D. and Agrawal,S. S 2012. Multibiometric Identification System Based On Score Level Fusion. IOSR Journal of Electronics and Communication Engineering (IOSRJECE). 07-11.
7. Marcialis,G. L. and Roli,F. Score-level fusion of fingerprint and face matchers for personal verification under "stress" conditions.
8. Gowtham,P. ,Sindhu,C. ,Sudheer Kumar,Chandra Sekhar, and Abdullah , M. S. 2013. identification of finger images using score-level fusion. international journal of engineering science invention . 12 -23.
9. yadav,S. S, Gothwal ,J. K. and R. Singh2011. Multimodal Biometric Authentication System:Challenges and Solutions. Global Journal of Computer Science andTechnology. .
10. . Antonio, J. U. , Hartung, d. ,Pascual, j. e. s and R. S. Reillo, "vascular biometrics based on a minutiae extraction approach".
11. O. M. Aly,H. M. Onsi,G. I. Salama,and Mahmoud, T. A. 2012. multimodal biometric system using iris, palmprint and finger-knuckle. International Journal of Computer Applications . 1-6.
12. Xiangy,W. ,. DesaiyPaul,afengWangz,and Peng T. A Prototype Biometric Security Authentication System Based Upon Fingerprint recognition.
13. Kekre,H. B. ,Thepade,H. D. , Maloo. A. Eigenvectors of Covariance Matrix using Row Mean and Column Mean Sequences for Face Recognition. International Journal of Biometrics and Bioinformatics (IJBB). 42-50.
14. Ajay Kumar, and Yingbo Zhou2012. Human Identification Using Finger Images. IEEE Transactions on Image Processing.
15. Ross,A. , Nandakumar,K. and Jain. A. K. 2006. Handbook of Multibiometrics. Springer, New York, USA, 1st edition, 2006.

Fingerprint Finger-vein Multimodal Biometrics Score Level Fusion Fusion With Alignment