FUZZY LOGIC-BASED FUSION BIOMETRIC

In chapter six, the Markov chain based rank level fusion method has been introduced. The basics of Markov chain have been discussed and its structure mechanism in the context of multimodal biometric rank fusion has been shown. This method demonstrates a number of compensation over other rank fusion approaches in terms of acknowledgment presentation. Furthermore, this method satisfies the Condorcet measure, which is necessary in any fair rank information fusion process. In this chapter, another new biometric fusion approach based on fuzzy logic is discussed and hence named as fuzzy fusion for multibiometrics.

          Fuzzy fusion method is one of sub-branches of information fusion, which has lately emerged as information consolidation tool. Most fuzzy fusion methods reported in the journalism are residential for areas such as habitual target acknowledgment, biomedical image fusion and segmentation, gas turbine power plants fusion, weather forecasting, aerial image repossession and classification, vehicle detection and classification, and path planning. In the circumstance of biometric authentication, fuzzy logic based fusion approach has recently been used for superiority based biometric information consolidation process. In Monwar, Gavril ova, and Wang (2011), the fuzzy fusion method is utilized in multimodal biometric system.

         Fuzzy logic refers to the theories and technologies that employ fuzzy sets, which are classes with un-sharp limitations. The idea of fuzzy sets was introduced in 1965 by Professor Lotfi A. Zadeh from the University of California, Berkeley. The core technique of fuzzy logic is based on following four basic concepts:

·         Fuzzy Sets: A fuzzy set is a set with a smooth border line. Fuzzy set theory generalizes the classical set theory to allow fractional membership.

·         Linguistic Variable: A linguistic unpredictable in one which allows its value to be described both qualitatively by a linguistic term and quantitatively by a corresponding membership function (which represents the meaning of the fuzzy set).

         Fuzzy logic is indeed one of the fascinating areas on the edge stuck between cognitive science and decision making. Utilizing morality of fuzzy logic for information fusion allows emulating the abstract calculation and complex human intellect processes by the means of a range of values between yes or no, or true and false. In a machine language, it is a difference between 0 and 1, and in biometric terms—between accepted and refused individuality, or granted or denied access to a secure building or a facility.

         One of the first works in this domain which has implication for decision-making is 1999 work by Solaiman et al. The authors projected a fuzzy-based multisensory data fusion classifier for to be used in a geo-spatial and remote sensing domain for land cover classification. Their classifier provided a tool for incorporation of multisensory and contextual information. The authors introduced the Fuzzy Membership Maps (FMMs) to represent different thematic classes based on a priori information obtained from sensors. The FMMs were next iteratively updated using spatial appropriate information. The fuzzy logic allowed their projected classifier to incorporate multisensory and a priori information.