DeFraudNet:End2End Fingerprint Spoof Detection using Patch Level Attention
This addresses security vulnerabilities in fingerprint recognition systems against spoof attacks, with incremental improvements in cross-sensor and cross-material detection.
The paper tackles the problem of fingerprint spoof detection by proposing a method using global and local feature descriptors extracted via DenseNet and a patch attention network, achieving average accuracies of 99.52%, 99.16%, and 99.72% on LivDet 2017, 2015, and 2011 datasets, outperforming state-of-the-art by 3-4% on some datasets.
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.