Multibiometric fusion strategy and its applications: A review

The unimodal biometric based system faced several inherent problems like lack of uniqueness, intra-class variation, non-universality, noisy data (presence of dirt on the sensor), restricted degree of freedom, unacceptable error rate, failure-to-enroll and spoofing attack. Multibiometric is one of the best choices to overcome these problems. Multibiometric fusion plays an important role to enhance the overall performance of the system, in which two or more individual biometric are combined together to form a better performance system. The proper use of fusion strategy is very important in the multibiometric system because it can affect the overall performance and accuracy level of the systems. In designing a multibiometric based system we can use various methods and fusion strategies to combine information from multiple sources. This paper is an in-depth study on multibiometric (multimodal, multialgorithm, multi-sample, multi-sensor and multi-instance) fusion strategy and its different applications. In addition, this paper also discusses the different methodology used in a fusion process (Sensor, Feature, Score, Decision, Rank) of multibiometric systems from last three decades and examines the methods used, to explore their successes and failure.