|International Conference and Workshop on Emerging Trends in Technology
|Foundation of Computer Science USA
|ICWET2012 - Number 4
|Authors: Smita Thakre, Kalyani Mamulkar, Prachi Motghare, Pooja Godi, Ujwalla Gawande
Smita Thakre, Kalyani Mamulkar, Prachi Motghare, Pooja Godi, Ujwalla Gawande . Multimodal Biometric Feature based Person Classification. International Conference and Workshop on Emerging Trends in Technology. ICWET2012, 4 (March 2012), 41-45.
A Monomodal Biometric system encounters a variety of security problems and presents sometimes unacceptable error rates. Conventional biometric system tends to have larger memory footprint, slower processing speed, and higher implementations and operational costs. Multiple biometric consist in combining two or more biometric modalities in a single identification system to improve the recognition accuracy. Whereas a state of art of framework for multimodal biometric identification system which can be adapted for any type of biometrics to provide smaller memory footprints and faster implementations than the conventional multimodal biometrics systems. In these paper we extract the feature of iris and fingerprint and fuse them at feature level and utilize SVM(Support Vector Machine) classifier for matching purpose to provide a higher accuracy than unimodal system.