Publication Type:

Journal Article

Source:

International Journal of Applied Engineering Research, Research India Publications, Volume 10, Number 8, p.21341-21354 (2015)

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-84929934274&partnerID=40&md5=59607c9dcdf39f28aa9062be1324c6c1

Abstract:

Multimodal biometrics has become an interest of areas for researches in the recent past as it provides more reliability and accuracy. In this work, we have performed multimodal biometric score fusion with the help of neural networks. The two traits that have been selected for fusion are fingerprint and iris due to their effectiveness and good resistance to spoofing. The type of fusion employed in the system is score level fusion. The neural network classifier approach is chosen to take advantage of its good learning efficiency. The system trains the neural network using a recently developed evolutionary algorithm, the Cuckoo Search Algorithm. The experimental results shown that the proposed fusion system can provide us low FAR, FRR and maximum accuracy of 98.78%. © Research India Publications.

Notes:

cited By 0

Cite this Research Publication

J. Aravinth and Valarmathy, Sb, “A Natural Optimization Algorithm to Fuse Scores for Multimodal Biometric Recognition”, International Journal of Applied Engineering Research, vol. 10, pp. 21341-21354, 2015.