  In the field of biometrics, fingerprint recognition systems are vulnerable to
presentation attacks made by artificially generated spoof fingerprints.
Therefore, it is essential to perform liveness detection of a fingerprint
before authenticating it. Fingerprint liveness detection mechanisms perform
well under the within-dataset environment but fail miserably under cross-sensor
(when tested on a fingerprint acquired by a new sensor) and cross-dataset (when
trained on one dataset and tested on another) settings. To enhance the
generalization abilities, robustness and the interoperability of the
fingerprint spoof detectors, the learning models need to be adaptive towards
the data. We propose a generic model, EaZy learning which can be considered as
an adaptive midway between eager and lazy learning. We show the usefulness of
this adaptivity under cross-sensor and cross-dataset environments. EaZy
learning examines the properties intrinsic to the dataset while generating a
pool of hypotheses. EaZy learning is similar to ensemble learning as it
generates an ensemble of base classifiers and integrates them to make a
prediction. Still, it differs in the way it generates the base classifiers.
EaZy learning develops an ensemble of entirely disjoint base classifiers which
has a beneficial influence on the diversity of the underlying ensemble. Also,
it integrates the predictions made by these base classifiers based on their
performance on the validation data. Experiments conducted on the standard high
dimensional datasets LivDet 2011, LivDet 2013 and LivDet 2015 prove the
efficacy of the model under cross-dataset and cross-sensor environments.
