An adaptive fuzzy multimodal biometric system for identification and verification

Mehdi Ghayoumi, Kambiz Ghazinour

Abstract: Biometric data are the sensitive personal information and the large intra-class variability due to changes of the environment conditions is an issue in these type of data. Adaptive biometric is the solution that has been introduced and can make the systems more accurate and reliable. For this purpose, semi-supervised learning has been shown to be a possible strategy. On the other hand, one problem in semi-supervised learning is selecting the decision threshold for adaption which can make the strategy unstable. In particular, a strong classifier, in a multimodal system, is better if adapted threshold is replaced with an inflexible one. This paper presents a fuzzy system to find the better threshold for adaptation. Experiments on MOBIO face and speech database show that the proposed strategy is a better approach in comparison to normal adaptive method.