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In the field of biometrics, fingerprint recognition systems are vulnerable topresentation attacks made by artificially generated spoof fingerprints.Therefore, it is essential to perform liveness detection of a fingerprintbefore authenticating it. Fingerprint liveness detection mechanisms performwell under the within-dataset environment but fail miserably under cross-sensor(when tested on a fingerprint acquired by a new sensor) and cross-dataset (whentrained on one dataset and tested on another) settings. To enhance thegeneralization abilities, robustness and the interoperability of thefingerprint spoof detectors, the learning models need to be adaptive towardsthe data. We propose a generic model, EaZy learning which can be considered asan adaptive midway between eager and lazy learning. We show the usefulness ofthis adaptivity under cross-sensor and cross-dataset environments. EaZylearning examines the properties intrinsic to the dataset while generating apool of hypotheses. EaZy learning is similar to ensemble learning as itgenerates an ensemble of base classifiers and integrates them to make aprediction. Still, it differs in the way it generates the base classifiers.EaZy learning develops an ensemble of entirely disjoint base classifiers whichhas a beneficial influence on the diversity of the underlying ensemble. Also,it integrates the predictions made by these base classifiers based on theirperformance on the validation data. Experiments conducted on the standard highdimensional datasets LivDet 2011, LivDet 2013 and LivDet 2015 prove theefficacy of the model under cross-dataset and cross-sensor environments.

EaZy Learning: An Adaptive Variant of Ensemble Learning for Fingerprint Liveness Detection