Multimodal Biometric Systems: A Comparative Study

Biometrics technology stands as one of the major backbones that had united biosciences and technology representing an instrument for security and forensics researchers to develop more accurate, robust and confident systems. Starting from uni-modal biometrics as finger print, face, speech and iris passing through multimodal biometrics based on uni-biometrics fused by different fusion techniques as feature level, score level and decision level fusion techniques, biometrics were still one of the most investigated technologies. From here in this paper, we tried to build the base for researchers whom are interested in biometric systems through introducing a comparative study of most used and known uni- and multimodal biometrics such as face, iris, finger vein, face and iris multimodal, face, finger print and finger vein multimodal. Through this comparative study, a comparative model is based on principal component analysis feature extractor and Euclidean distance matcher applied using MATLAB. This model was trained and tested in two different modes homogenous data using SDUMLA-HMT database and heterogeneous mode extracting 106 frontal single face image from CASIA-FACEV5 while the reminder biometrics under consideration from SDUMLA-HMT. Feature level and score level fusions were tested in both modes on all multimodal systems under consideration.