Implementation and Analysis of Fusion in Multibiometrics

Biometrics are being implemented to overcome the major security concerns faced by the traditional security systems such as passwords and pincode. However, over the past few years, many researchers have deployed unimodal biometrics, which makes use of only one biometric trait. Despite the fact that unimodal biometrics have solved many security issues, they are experiencing several challenges such high error rates, noisy data and intra-class variations. To address these issues, researchers are exploring multimodal biometrics, which makes use of more than one biometric trait. Despite ongoing researches, the most appropriate level at which fusion has to be done in multibiometric has still not been established. In this work, the four levels of fusions, that is, sensor level, feature level fusion, score level and decision level are being explored and experimented on various datasets using face biometrics. The performance at each level is being evaluated and the results reveal that score level fusion beats the other levels of fusion by providing a recognition rate of nearly 98.9.