Biometric authentication systems are pervasive in modern society, but they are quite vulnerable to spoofing attacks. Research on spoofing (or liveness) detection is therefore very active. A number of methods have been proposed in the literature, sometimes with very promising results, but limited robustness with respect to the large variety of biometric traits, sensors, and attacks encountered in real-life. Recently, methods based on Convolutional Neural Networks (CNNs) are drawing great attention, given their success in many other image processing tasks. However, despite some promising results, they seem to suffer the same robustness problem, requiring heavy training to work properly. Here, we propose a new CNN architecture for biometric spoofing detection. Thanks to domain-specific knowledge, accounted for through a suitable loss function, a compact architecture is obtained, allowing reliable training also in the presence of small-size datasets. Experiments prove the proposal to provide state-of-art performance on several widespread datasets for face and iris liveness detection. © 2016 IEEE.

Biometric Spoofing Detection by a Domain-Aware Convolutional Neural Network

Gragnaniello D;
2017-01-01

Abstract

Biometric authentication systems are pervasive in modern society, but they are quite vulnerable to spoofing attacks. Research on spoofing (or liveness) detection is therefore very active. A number of methods have been proposed in the literature, sometimes with very promising results, but limited robustness with respect to the large variety of biometric traits, sensors, and attacks encountered in real-life. Recently, methods based on Convolutional Neural Networks (CNNs) are drawing great attention, given their success in many other image processing tasks. However, despite some promising results, they seem to suffer the same robustness problem, requiring heavy training to work properly. Here, we propose a new CNN architecture for biometric spoofing detection. Thanks to domain-specific knowledge, accounted for through a suitable loss function, a compact architecture is obtained, allowing reliable training also in the presence of small-size datasets. Experiments prove the proposal to provide state-of-art performance on several widespread datasets for face and iris liveness detection. © 2016 IEEE.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4776933
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 20
  • ???jsp.display-item.citation.isi??? 17
social impact