Abstract
Multimodal biometric systems are considered a way to minimize the limitations raised by single traits. This paper proposes new schemes based on score level, feature level and decision level fusion to efficiently fuse face and iris modalities. Log-Gabor transformation is applied as the feature extraction method on face and iris modalities. At each level of fusion, different schemes are proposed to improve the recognition performance and, finally, a combination of schemes at different fusion levels constructs an optimized and robust scheme. In this study, CASIA Iris Distance database is used to examine the robustness of all unimodal and multimodal schemes. In addition, Backtracking Search Algorithm (BSA), a novel population-based iterative evolutionary algorithm, is applied to improve the recognition accuracy of schemes by reducing the number of features and selecting the optimized weights for feature level and score level fusion, respectively. Experimental results on verification rates demonstrate a significant improvement of proposed fusion schemes over unimodal and multimodal fusion methods.
Highlights
The recognition of human beings based on physical and/or behavioral characteristics is a trend in places with high security needs
We investigate the effect of information fusion on face- iris modalities at different levels of fusion in order to improve the recognition performance and solve the problems raised by unimodal face and iris traits
The images in this database captured a high-resolution camera, so both dual-eye iris images in this database havehave beenbeen captured by a by high-resolution camera, so both dual-eye iris and and face patterns are available in the image region with detailed facial features that is appropriate for face patterns are available in the image region with detailed facial features that is appropriate for multimodal biometric information fusion
Summary
The recognition of human beings based on physical and/or behavioral characteristics is a trend in places with high security needs. Unimodal biometric systems, which use single-source biometric traits, usually suffer due to several factors such as a lack of uniqueness, non-universality and noisy data [1]. In this respect, multimodality can be employed as a remedy in order to solve the limitations of unimodal systems and improve the system performance by extracting the information from multiple biometric traits. We investigate the effect of information fusion on face- iris modalities at different levels of fusion in order to improve the recognition performance and solve the problems raised by unimodal face and iris traits
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