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Fuzzy commitment schemes have been established as a reliable means of binding cryptographic keys to binary feature vectors extracted from diverse biometric modalities. In addition, attempts have been made to extend fuzzy commitment schemes to incorporate multiple biometric feature vectors. Within these schemes potential improvements through feature level fusion are commonly neglected. In this paper a feature level fusion technique for fuzzy commitment schemes is presented. The proposed reliabilitybalanced feature level fusion is designed to re-arrange and combine two binary biometric templates in a way that error correction capacities are exploited more effectively within a fuzzy commitment scheme yielding improvement with respect to key-retrieval rates. In experiments, which are carried out on iris-biometric data, reliability-balanced feature level fusion significantly outperforms conventional approaches to multi-biometric fuzzy commitment schemes confirming the soundness of the proposed technique.

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