A comparative study of feature level fusion strategies for Multimodal Biometric Systems based on Face and Iris

Abstract

With the technology advances, new approaches for automatic recognition of a person's identity have been proposed and such a fact has encouraged the use of Biometrics Systems. This approach uses physical or behavioural characteristics of the user in order to recognize or authenticate their identity. The Biometric Systems can be classified as Unimodal or Multimodal. The Unimodal Systems use a single biometric modality to perform the recognition, while the Multimodal ones use two or more modalities. A Multimodal Biometric System can be constructed in different ways, according to its architecture, fusion level and fusion strategies. The main of this work is to investigate and compare different feature level fusion strategies, in order to design a Multimodal Biometric System with high performance. In this paper, we used the discrete wavelet transform to extract the feature sets from iris and face images. Experimental results show that Multimodal Biometric Systems outperform Unimodal Biometric Systems according to recognition rate computed over the outputs produced by the induced Support Vector Machine classifier.