Mitigating replay attacks using Darwinian-based Feature Extraction

In [1,2], a Genetic and Evolutionary Biometric Security (GEBS) application was presented for preventing biometric replay attacks. This technique used Genetic and Evolutionary Feature Extraction - Machine Learning (GEFE
_ML
) to create disposable feature extractors (FEs). These disposable FEs had higher recognition accuracy than a traditional feature extraction approach, known as the local binary pattern method. In [3], a two-stage process for developing FEs was developed. This technique is known as Darwinian Feature Extraction (DFE), and it created Darwinian FEs (dFEs) that had even higher recognition accuracy than GEFE
_ML
while maintaining a lower computational complexity. In this paper, we apply dFEs towards mitigating replay attacks and compare the results to disposable FEs using GEFE
_ML
. Our results show the effectiveness of GEFE
_ML
and DFE towards creating dFEs.