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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Hybrid Fusion Framework for Iris Recognition Systems
verfasst von : He Zhang, Jing Liu, Zhiguo Zeng, Qianli Zhou, Shengguang Li, Xingguang Li, Hui Zhang
Erschienen in: Biometric Recognition
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Abstract
Due to the advantages in uniqueness, convenience and non-contact, iris recognition is widely deployed for automatic identity authentication. Instead of a single signature, multiple templates are registered in real-world applications for the diversity of gallery samples, resulting in great enhanced user experience. In this paper, we exploit the connection among the multiple registration data and then make efforts to give a more comprehensive decision based on them. A novel hybrid fusion framework is proposed to fuse information at groups in feature and score levels. Specifically, the gallery samples are firstly divided into groups to balance the abundance and the robustness of information. Afterwards, hierarchical fusion is performed at the groups, which is actually the procedure of information mapping and reducing. The experimental results demonstrate the effectiveness and generalization ability of the proposed hybrid fusion framework.