Deep Representations for Iris, Face, and Fingerprint Spoofing Detection


Biometrics systems have significantly improved person identification and authentication, taking part in an necessary role in personal, national, and international security. However, these systems would possibly be deceived (or spoofed) and, despite the recent advances in spoofing detection, current solutions often depend on domain data, specific biometric reading systems, and attack varieties. We tend to assume a very limited knowledge concerning biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based mostly on 2 deep learning approaches. The primary approach consists of learning suitable convolutional network architectures for each domain, whereas the second approach focuses on learning the weights of the network via back propagation. We have a tendency to think about nine biometric spoofing benchmarks—every one containing real and fake samples of a given biometric modality and attack type—and learn deep representations for every benchmark by combining and contrasting the 2 learning approaches. This strategy not solely provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known leads to eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks will be robust to attacks already known and possibly tailored, with little effort, to image-based attacks that are nonetheless to return.

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