BIOMETRICS

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%.