  Incremental learning enables the learner to accommodate new knowledge without
retraining the existing model. It is a challenging task which requires learning
from new data as well as preserving the knowledge extracted from the previously
accessed data. This challenge is known as the stability-plasticity dilemma. We
propose AILearn, a generic model for incremental learning which overcomes the
stability-plasticity dilemma by carefully integrating the ensemble of base
classifiers trained on new data with the current ensemble without retraining
the model from scratch using entire data. We demonstrate the efficacy of the
proposed AILearn model on spoof fingerprint detection application. One of the
significant challenges associated with spoof fingerprint detection is the
performance drop on spoofs generated using new fabrication materials. AILearn
is an adaptive incremental learning model which adapts to the features of the
``live'' and ``spoof'' fingerprint images and efficiently recognizes the new
spoof fingerprints as well as the known spoof fingerprints when the new data is
available. To the best of our knowledge, AILearn is the first attempt in
incremental learning algorithms that adapts to the properties of data for
generating a diverse ensemble of base classifiers. From the experiments
conducted on standard high-dimensional datasets LivDet 2011, LivDet 2013 and
LivDet 2015, we show that the performance gain on new fake materials is
significantly high. On an average, we achieve $49.57\%$ improvement in accuracy
between the consecutive learning phases.
