A Bayesian approach for modeling sensor influence on quality, liveness and match score values in fingerprint verification

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Towards anomaly detection for increased security in multibiometric systems: Spoofing-resistant 1-median fusion eliminating outliers (2014, background): “[13] claimed that image quality, liveness measurement and match scores are normally influenced by the sensor.”
Automatic adaptation of fingerprint liveness detector to new spoof materials (2014, background): “This is because (a) existing fingerprint liveness detection algorithms do not exhibit acceptable error rates [22, 17, 8, 18], and (b) the performance of these liveness detectors is significantly influenced by the fabrication materials used to generate spoofs during the training stage.”