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We discuss the problem of combining biometric match scores with liveness measure values in the context of fingerprint verification. Recent literature has focused on the development of methods to assess if an input fingerprint sample is a “live” entity or a “spoof” artefact. This is commonly done by generating a single-valued(More)
Multibiometric systems, which consolidate or fuse multiple sources of biometric information, typically provide better recognition performance than unimodal systems. While fusion can be accomplished at various levels in a multibiometric system, score-level fusion is commonly used as it offers a good tradeoff between data availability and ease of fusion. Most(More)
While fusion can be accomplished at multiple levels in a multibiometric system, score level fusion is commonly used as it offers a good trade-off between fusion complexity and data availability. However, missing scores affect the implementation of several biometric fusion rules. While there are several techniques for handling missing data, the imputation(More)
A fingerprint recognition system is vulnerable to spoof attacks, where a fake fingerprint is used to circumvent the system. To counter such attacks, an automated spoof detector is used to distinguish images of fake fingerprints from those of real live fingerprints. Most spoof detectors adopt a machine learning approach, where a classifier is trained to(More)
Recent research has sought to improve the resilience of fingerprint verification systems to spoof attacks by combining match scores with both liveness measures and image quality in a learning-based fusion framework. Designing such a fusion framework is challenging because quality and liveness measures can impact the match scores and, therefore, the(More)
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