  In recent years, fingerprint recognition systems have made remarkable
advancements in the field of biometric security as it plays an important role
in personal, national and global security. In spite of all these notable
advancements, the fingerprint recognition technology is still susceptible to
spoof attacks which can significantly jeopardize the user security. The cross
sensor and cross material spoof detection still pose a challenge with a myriad
of spoof materials emerging every day, compromising sensor interoperability and
robustness. This paper proposes a novel method for fingerprint spoof detection
using both global and local fingerprint feature descriptors. These descriptors
are extracted using DenseNet which significantly improves cross-sensor,
cross-material and cross-dataset performance. A novel patch attention network
is used for finding the most discriminative patches and also for network
fusion. We evaluate our method on four publicly available datasets:LivDet 2011,
2013, 2015 and 2017. A set of comprehensive experiments are carried out to
evaluate cross-sensor, cross-material and cross-dataset performance over these
datasets. The proposed approach achieves an average accuracy of 99.52%, 99.16%
and 99.72% on LivDet 2017,2015 and 2011 respectively outperforming the current
state-of-the-art results by 3% and 4% for LivDet 2015 and 2011 respectively.
