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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 (GEFEML) 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 GEFEML while maintaining a lower computational complexity. In this paper, we apply dFEs towards mitigating replay attacks and compare the results to disposable FEs using GEFEML. Our results show the effectiveness of GEFEML and DFE towards creating dFEs.