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Deep representations for iris, face, and fingerprint spoofing detection

Grant number: 15/03036-1
Support type:Regular Research Grants - Publications - Scientific article
Duration: April 01, 2015 - June 30, 2015
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Alexandre Xavier Falcão
Grantee:Alexandre Xavier Falcão
Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil


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, while 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. (AU)