BIOMETRIC INFORMATION FUSION

Information fusion can be defined as “an information process that associates, correlates and combines data and information from single or multiple sensors or sources to achieve refined estimates of parameters, characteristics, events and behaviors”. A good information fusion method allows the impact of less reliable sources be lowered compared to reliable ones. A number of disparate research areas including robotics, image processing, pattern recognition, information retrieval etc. utilize and describe information fusion in their context. Thus, information fusion established itself as an independent research area over the last decade for its impact on a vast number of disparate research areas. For example, the concept of data and feature fusion initially occurred in multi-sensor processing. In fact, information fusion was for a long time used in engineering and signal processing fields, as well as in decision-making and expert systems. By now, several other research fields found its application useful. Besides the more classical data fusion approaches in robotics, image processing and pattern recognition, the information retrieval community has been known to combine multiple information sources. The basic building block of an information fusion system which fuses source information at the early stage of the system?

BIOMETRIC INFORMATION FUSION

Due to some problems associated with the unimodal biometric data, such as small variation over the population, large intra-variability over time, absence of biometric sample in portion of a population etc., the use of multimodal biometrics is a first choice solution. The main objective of a multimodal biometric system is to improve the recognition performance of the system and to make the system robust over the limitations associated with unimodal biometric systems. Over the years, several approaches have been proposed and developed for multimodal biometric authentication system with different biometric traits and with different fusion mechanisms.

Multimodal biometric systems use multiple sources of biometric information, whereas information fusion is essential for analysis, indexing and retrieval of such information . There are numbers of fusion techniques for any particular information. Choosing appropriate fusion techniques for any specific information depends on the necessity of the application and the performance of the fusion techniques proven by previous research. There is a consensus in biometric literature that all various levels of multimodal biometric information fall into two broad categories: before matching and after matching fusion. Fusion before matching category contains sensor level fusion and feature level fusion, while fusion after matching contains match score level fusion, rank level fusion and decision level fusion. A novel fusion mechanism has been established recently in BT Lab is based on fuzzy logic fusion, and hence named a fuzzy biometric fusion. Fuzzy biometric fusion can be employed either in the initial stage, i.e. before matching occurred or in the latter stage, i. e. after matching occurred.