Multimodal Biometric Technology Using Fuzzy Logic Decision and Fuzzy Inference System

Abstract

In this research paper I discuss about the challenges to implement secure personal identification protocols with biometric technology are increasing and the need for accurate human identification is higher than ever. Single modality biometric systems have to contend with a variety of problems such as noisy data, intra class variations, non-universality, spoof attacks, and distinctiveness. Some of these limitations can be addressed by deploying multimodal biometric systems that integrate multiple biometric modalities in a single scan to alleviate the challenges of a unimodal system. Performance in biometric verification is often affected by external conditions and variabilities. These are often related to mismatched conditions between enrolment and verification sessions, e.g. handsets/microphones for recording speech, cameras for capturing facial images and fingerprint readers. In addition, the user’s speech may vary according to ambient noise conditions, the speaker’s health (e.g. contracting a cold) or speaking styles. The user’s facial images may vary due to changes in backgrounds, illumination, head positions and expressions. While none of the biometrics alone can guarantee absolute reliability, they can reinforce one another when used jointly to maximize verification performance. This motivates multi-biometric (multimodal) authentication.