Fingerprint liveness map construction using convolutional neural network

Jung, Ho Yub; Heo, Yong Seok

doi:10.1049/el.2018.0621

Cited by 40 publications

(19 citation statements)

References 2 publications

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“…Previous studies on fingerprint synthesis have been mostly associated with manual generation from a single or multiple fingerprint base structure. These studies show that using morphological or minutiae point manipulation, we can produce synthetic fingerprints [3]- [6].…”

Section: Introductionmentioning

confidence: 88%

A lightweight GAN network for large scale fingerprint generation

Fahim

Jung

2020

IEEE Access

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Generating fingerprint images for biometric purposes is both necessary and challenging. In this study, we presented a fingerprint generation approach based on generative adversarial network. To ensure GAN training stability, we have introduced conditional loss doping that allows a continuous flow of gradients. Our study utilizes a careful combination of a residual network and spectral normalization to generate fingerprints. The proposed average residual connection shows more immunity against vanishing gradients than a simple residual connection. Spectral normalization allows our network to enjoy reduced variance in weight generation, which further stabilizes the training. Proposed scheme uses spectral bounding only in the input and the fully connected layers. Our network synthesized fingerprints up to 256 by 256 in size. We used the multi-scale structural similarity (MS-SSIM) metric for measuring the diversity of the generated samples. Our model has achieved 0.23 MS-SSIM scores for the generated fingerprints. The MS-SSIM score indicates that the proposed scheme is more likely to produce more diverse images and less likely to face mode collapse.

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Section: Introductionmentioning

confidence: 88%

A lightweight GAN network for large scale fingerprint generation

Fahim

Jung

2020

IEEE Access

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“…Jung and Heo [28] introduced a convolutional neural network (CNN) architecture to deal with the liveness detection issue. The proposed architecture is a robust framework for training and detection.…”

Section: Attacks To User Interface and Countermeasuresmentioning

confidence: 99%

“…In this section, as the countermeasure to spoofing attacks, several liveness detection methods are reviewed. Non-machine learning based algorithms [24][25][26][27]29] and machine learning based algorithms [28,30,31] were proposed to extract unique features to ascertain whether an input fingerprint is fake or real. The three machine learning based algorithms [28,30,31] were all recently published (in 2018), which shows that machine learning is playing an active role in liveness detection design.…”

Section: Attacks To User Interface and Countermeasuresmentioning

confidence: 99%

Security and Accuracy of Fingerprint-Based Biometrics: A Review

et al. 2019

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Biometric systems are increasingly replacing traditional password-and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper. recognition as an example. In the stage of enrollment, a user presents their finger to the fingerprint sensor and a fingerprint image is acquired by the sensor module. Certain features of the acquired fingerprint image are extracted, and further adapted or transformed to generate template data for the purpose of comparison in the verification stage. In the verification stage, the fingerprint image of a query is collected by the sensor module. The feature representations of the query fingerprint image go through the same process as in the enrollment stage, so as to obtain query data. The query data are then compared with the template data so that a matching outcome is attained.

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“…Convolutional neural networks (CNN) have shown to produce accurate liveness classification models without any prior knowledge or study of spoof and liveness features [4] [5]. CNN and deep learning methods are convenient to use because it is not necessary to extract definitive features for classification problems.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we first train a CNN to generate the liveness map of the fingerprint using a network architecture and loss function similar to our previous work [5]. Then, the liveness maps are obtained for template and probe fingerprints.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Liveness Detection by a Template-Probe Convolutional Neural Network

2019

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Fingerprints are known to be easily synthesized to trick identification systems. In this paper, we propose a new method that incorporates template fingerprints stored for identification in the liveness detection system. The fingerprint identification platform must have a list of template fingerprints stored for matching with new probe fingerprints trying to access the system. Thus, instead of simply detecting the liveness of the probe fingerprints, the proposed approach uses the matching template fingerprints along with probe fingerprints through convolutional neural networks to make the liveness decision, which comprises two sequential convolutional neural networks for classification. The proposed method can be built on the top of existing liveness detection methods to increase accuracy without a significant increase in computation time. The evaluation over the LivDet dataset shows that the proposed fingerprint liveness detection method is able to obtain state-of-the-art accuracy.

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Fingerprint liveness map construction using convolutional neural network

Cited by 40 publications

References 2 publications

A lightweight GAN network for large scale fingerprint generation

A lightweight GAN network for large scale fingerprint generation

Security and Accuracy of Fingerprint-Based Biometrics: A Review

Fingerprint Liveness Detection by a Template-Probe Convolutional Neural Network

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