Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

By David Menotti, Giovani Chiachia, Allan da Silva Pinto, William Robson Schwartz, Hélio Pedrini, Alexandre Xavier Falcão, Anderson de Rezende Rocha. IEEE Transactions on Information Forensics and Security, 2014

476 citations recorded by the source corpus.

Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or spoofed) and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first 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 consider nine biometric spoofing benchmarks - each one containing real and fake samples of a given biometric modality and attack type - and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3d30462db19027575f0c1c2c14bf33c57878657b

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Citations in the corpus

Fingerprint Liveness Detection Using Convolutional Neural Networks (2016, background): “, not learned) weights (also explored in [13]), and Local”
Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos (2018, methodology): “That of Menotti et al. [19] uses a similar procedure but optimizes the ﬁlters in an available high-performance CNN architecture.”