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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)
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)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t Multibiometric systems, which consolidate or fuse multiple sources of(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)
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)
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