Abstract
The rapid growth of fingerprint authentication-based applications makes presentation attack detection, which is the detection of fake fingerprints, become a crucial problem. There have been numerous attempts to deal with this problem; however, the existing algorithms have a significant trade-off between accuracy and computational complexity. This paper proposes a presentation attack detection method using Convolutional Neural Networks (CNN), named fPADnet (fingerprint Presentation Attack Detection network), which consists of Fire and Gram-K modules. Fire modules of fPADnet are designed following the structure of the SqueezeNet Fire module. Gram-K modules, which are derived from the Gram matrix, are used to extract texture information since texture can provide useful features in distinguishing between real and fake fingerprints. Combining Fire and Gram-K modules results in a compact and efficient network for fake fingerprint detection. Experimental results on three public databases, including LivDet 2011, 2013 and 2015, show that fPADnet can achieve an average detection error rate of 2.61%, which is comparable to the state-of-the-art accuracy, while the network size and processing time are significantly reduced.
Highlights
Authentication systems that use fingerprint recognition are presently evaluated as an authentication method with outstanding growth thanks to the ease of use and economic advantages of low setup costs
This paper proposes a network, named fPADnet, that can discover fake fingerprints with a high detection rate, in a reasonable time, with low storage usage; it can be integrated with fingerprint sensors. fPADnet uses SqueezeNet [7], which has 100× fewer parameters than that of VGG [8], as the base architecture to minimize the network size and computational time
Liveness Detection competition (LivDet) 2011 consists of four datasets captured using four different sensors (Biometrika, Digital Persona, ItalData and Sagem)
Summary
Authentication systems that use fingerprint recognition are presently evaluated as an authentication method with outstanding growth thanks to the ease of use and economic advantages of low setup costs. The previous studies have proven that fingerprint recognition systems are exposed to several security threats, such as attacking at fingerprint sensors using fake fingerprints (known as presentation attack), attacking the communication channels between modules, attacking the software modules and attacking the data storage [1,2,3]. This paper proposes a network, named fPADnet (fingerprint Presentation Attack Detection network), that can discover fake fingerprints with a high detection rate, in a reasonable time, with low storage usage; it can be integrated with fingerprint sensors. The main contributions of the proposed method are: fPADnet is suitable to deploy in real-world fingerprint recognition systems, especially embeddable in fingerprint sensors, thanks to its small size and low processing time.
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