Multimodal Biometrics for Robust Fusion Systems using Logic Gat es

Celik N Manivannan N

doi:10.4172/2155-6180.1000218

Abstract

Many professionals indicate that unimodal biometric recognition systems have many shortcomings associated with performance accuracy rates. In order to make the system design more robust, we propose a multimodal biometric which includes fingerprint and face recognition using logical AND operators at decision-level fusion. In this paper, we also discuss some concerns about the security issues regarding the identification and verification processes for the multimodal recognition system against invaders and threatening attackers. While the unimodal fingerprint and face biometric gives recognition rate of 94% and 90.8% respectively, the multi-modal approach was giving a recognition rate of 98% at the decision level fusion, showing an improvement in the accuracy. Also, both the FAR and FRR have been considerably reduced, showing that the multi-modal system implemented is more robust.

Highlights

Biometrics refers to the use of the physiological or behavioral characteristics of a person to authenticate his or her identity [1]
In a multimodal biometric system, information reconciliation can occur at the data or feature levels, at the match score level generated by multiple classifiers pertaining to different modalities, and at the decision level
The aim of this paper is to propose a new approach in order to increase the accuracy of multimodal biometric systems and reduce the insufficient accuracy of biometric traits, which are created by noisy data, using fusion levels algorithms between face and fingerprint recognition systems

Summary

Introduction

Biometrics refers to the use of the physiological or behavioral characteristics of a person to authenticate his or her identity [1]. The term ‘multimodal’ is used to describe the combination of two or more different biometric sources of a person (i.e. face, iris, fingerprint) sensed by different sensors. In a multimodal biometric system, information reconciliation can occur at the data or feature levels, at the match score level generated by multiple classifiers pertaining to different modalities, and at the decision level.

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