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

DeFraudNet:End2End Fingerprint Spoof Detection using Patch Level Attention