A DCNN Based Fingerprint Liveness Detection Algorithm with Voting Strategy

Wang, Chenggang; Li, Ké; Wu, Zhihong; Zhao, Qijun

doi:10.1007/978-3-319-25417-3_29

Cited by 42 publications

(17 citation statements)

References 17 publications

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“…Several proposals apply DNNs over the fingerprint images for the liveness detection problem. [55][56][57][58] In these proposals, the fingerprint images are divided into smaller patches that are processed independently, so as to increase the number of training examples and to simplify the processing. However, this strategy cannot be used for classification, as the class is derived from the global pattern shape of the fingerprint.…”

Section: Fingerprint Classification With Deep Neural Networkmentioning

confidence: 99%

On the use of convolutional neural networks for robust classification of multiple fingerprint captures

Peralta

Triguero

García

et al. 2017

Int. J. Intell. Syst.

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Fingerprint classification is one of the most common approaches to accelerate the identification in large databases of fingerprints. Fingerprints are grouped into disjoint classes, so that an input fingerprint is compared only with those belonging to the predicted class, reducing the penetration rate of the search. The classification procedure usually starts by the extraction of features from the fingerprint image, frequently based on visual characteristics. In this work, we propose an approach to fingerprint classification using convolutional neural networks, which avoid the necessity of an explicit feature extraction process by incorporating the image processing within the training of the classifier. Furthermore, such an approach is able to predict a class even for low-quality fingerprints that are rejected by commonly used algorithms, such as FingerCode. The study gives special importance to the robustness of the classification for different impressions of the same fingerprint, aiming to minimize the penetration in the database. In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state-of-the-art classifiers based on explicit feature extraction. The tested networks also improved on the runtime, as a result of the joint optimization of both feature extraction and classification. wileyonlinelibrary.com/journal/int

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Section: Fingerprint Classification With Deep Neural Networkmentioning

confidence: 99%

On the use of convolutional neural networks for robust classification of multiple fingerprint captures

Peralta

Triguero

García

et al. 2017

Int. J. Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…The new era of the image classification problem has started with the birth of Deep Convolutional Neural Networks (DCNN). DCNN have rapidly shown their effectiveness in fingerprint liveness detection [ 24 , 25 , 26 , 27 , 28 , 29 ]. There are two approaches to use convolutional neural networks in fake fingerprint detection.…”

Section: Literature Review On Presentation Attack Detectionmentioning

confidence: 99%

fPADnet: Small and Efficient Convolutional Neural Network for Presentation Attack Detection

Nguyen

Park

Cui

et al. 2018

Sensors

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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.

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“…Fewer patches also can be used instead of all patches of the grid. Wang et al [21] reveal the unsatisfying result of previous researchers in using local feature descriptor for liveness detection. Thus, the authors proposed a solution towards the fake fingerprint issue by using deep convolution neural network (DCNN) and voting strategy in feature extraction and classification step.…”

Section: The Fake Fingerprint Detection Software Approachesmentioning

confidence: 99%