Investigation of information fusion in face and palmprint multimodal biometrics

This paper reviews several information fusion techniques and strategies in the application of multimodal biometrics system using face and palmprint images. Multimodal biometric is able to overcome several limitations in single modal biometric such as intra-class variations, less discriminative power, noise data and redundant features. By consolidating two kinds of modality a better performance can be achieved. Information fusion in multimodal biometrics can be carried out at three possible levels, i.e. feature, matching score and decision levels. Fusions at these three levels have their own attributes, thus this paper is aimed to compare their effectiveness. A specific fusion rule is necessary to combine the information at each level. Several numbers of analyses on verification and identification shows matching score fusion is able to achieve the best performance which is 98% recognition rates and 98.5% GAR at 0.1% FAR when tested using AR face and PolyU palmprint datasets.