Biometrics is seen by many as a solution to a
lot of the user identification and security problems in todayUs networks.
Password abuse and misuse, intentional and inadvertent is a gaping hole in
network security. This results mainly from human error, carelessness and in
some cases maliciousness. Biometrics removes human error from the security
equation.
Our project will examine all the
technological and feasibility aspects as well as the practical
applications.
Biometric
systems allow automatic person recognition based on physical or behavioral
features which belong to a certain person. Each biometric feature has its
limits and no biometric system is perfect so unimodal biometric systems raise a
variety of problems. To over fulfilling some of the mentioned inconvenient and
limitations and to increase the level of security the multimodal biometric
systems are used. This paper proposes the multimodal biometrics system for
identity verification using two traits, i.e., speech signal and palmprint. The
proposed system is designed for applications where the training data contains a
speech signal and palmprint. The matching score level architecture uses weighted
sum of score technique. The features are extracted from the pre-processed palm
image and pre-processed speech signal. The features of a query image and speech
signal are compared with those of a database images and speech signal to obtain
matching scores. The individual scores generated after matching are passed to
the fusion module. This module consists of three major steps i.e.,
normalization, generation of similarity score and fusion of weighted scores.
The final score is then used to declare the person as genuine or an impostor.
The system is tested on database collected by the authors for 120 subjects and
gives an overall accuracy of 98.63% with FAR of 1.67% and FRR of 0.84%.