Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection

Gottschlich, Carsten

doi:10.1371/journal.pone.0148552

Cited by 16 publications

(13 citation statements)

References 49 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

“…In doing so, we would like to facilitate the reproducibility of the presented results and promote the comparability of fingerprint segmentation methods. Recently, this implementation of the FDB method has been applied to improve the performance of fingerprint liveness detection by the convolution comparison patterns [ 62 ] and fingerprint alteration detection [ 63 ]. The manually marked benchmark has been used by Thai and Gottschlich [ 64 ] and by Bartůněk [ 65 ] for evaluating a new fingerprint segmentation methods.…”

Section: Discussionmentioning

confidence: 99%

Filter Design and Performance Evaluation for Fingerprint Image Segmentation

Thai¹,

Huckemann²,

Gottschlich³

2016

PLoS ONE

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Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available.

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“…The residual image obtained by DG3PD contains the high-frequency components of the input image. Applications of DG3PD to iris or fingerprint liveness detection [48,49] are therefore very promising. A survey of local image descriptors for fingerprint, iris, and face liveness detection can be found in [50].…”

Section: Discussionmentioning

confidence: 99%

Directional global three-part image decomposition

Thai

Gottschlich

2016

J Image Video Proc.

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We consider the task of image decomposition, and we introduce a new model coined directional global three-part decomposition (DG3PD) for solving it. As key ingredients of the DG3PD model, we introduce a discrete multi-directional total variation norm and a discrete multi-directional G-norm. Using these novel norms, the proposed discrete DG3PD model can decompose an image into two or three parts. Existing models for image decomposition Advantages of the DG3PD model over existing ones lie in the properties enforced on the cartoon and texture images. The geometric objects in the cartoon image have a very smooth surface and sharp edges. The texture image yields oscillating patterns on a defined scale which are both smooth and sparse. Moreover, the DG3PD method achieves the goal of perfect reconstruction by summation of all components better than the other considered methods. Relevant applications of DG3PD are a novel way of image compression as well as feature extraction for applications such as latent fingerprint processing and optical character recognition.

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Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection

Cited by 16 publications

References 49 publications

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

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

Filter Design and Performance Evaluation for Fingerprint Image Segmentation

Directional global three-part image decomposition

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