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Multi-biometrics aims at building more accurate unified biometric decisions based on the information provided by multiple biometric sources. Information fusion is used to optimize the process of creating this unified decision. In previous works dealing with score-level multibiometric fusion, the scores of different biometric sources belonging to the comparison of interest are used to create the fused score. The novelty of this work focuses on integrating the relation of the fused scores to other comparisons within a 1:N comparison. This is performed by considering the neighbors distance ratio in the ranked comparisons set within a classification-based fusion approach. The evaluation was performed on the Biometric Scores Set BSSR1 database and the enhanced performance induced by the integration of neighbors distance ratio was clearly presented

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Multi-biometrics is the use of multiple biometric recognition sources to provide a more dependable verification or identification decision. Fusion of multi-biometric sources can be performed on different levels, such as the data, feature, or score level. This work presents an overview of the multi-biometric score-level fusion problem, along with the proposed solution in the literature. A discussion is made to provide a comparison between multi-biometric fusion in both scenarios. This discussion aims at providing a clearer view of future developments especially under the identification scenario where many related applications are rapidly growing such as forensics and ubiquitous surveillance.

Template protection is an important supplementary to biometric systems for enhancing security and privacy protection. Its recognition and security performance is limited by inherent properties of the biometric modalities and the biometric systems used. Combining additional secret information such as PIN or password will be a promising way to improve the performance.The fuzzy vault is a widely-used cryptographic scheme to protect fingerprint minutiae. In this paper, we propose a novel method, which generates artificial minutiae from a PIN or password. A fused feature set including genuine and artificial minutiae is used to generate a protected template. The insertion of artificial minutiae increases the secret length as well as the robustness in the fuzzy vault. Our experimental results on the NIST SD 14 database show that both false match rate and false non-match rate are reduced in comparison with the original fuzzy vault method. Meanwhile, the crucial security factors such as the degree of the polynomial and the length of secret are enlarged. Additionally, the original method is vulnerable to linkage attacks. The proposed method improves the resistance against this attack.