Fingerprint Liveness Detection by a Template-Probe Convolutional Neural Network

Jung, Ho Yub; Heo, Yong Seok; Lee, Soochahn

doi:10.1109/access.2019.2936890

Cited by 27 publications

(4 citation statements)

References 17 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: 87%

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: 87%

A lightweight GAN network for large scale fingerprint generation

Fahim

Jung

2020

IEEE Access

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“…A detailed comparison of the proposed method's performance with state-of-the-art methods is mentioned in Table 5. By Seeing 5 its is clearly evident that the classification performance of the proposed method is better than the method discussed in [17], [21], [12] [15], [23], [20], and [13] while the performance is comparable with the methods discussed in [22] and [28].…”

Section: Comparison With Existing Methods On Livdet 2015 Databasementioning

confidence: 92%

An open patch generator based fingerprint presentation attack detection using generative adversarial network

Rai,

Anshul,

Jha

et al. 2023

Multimed Tools Appl

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Automatic fingerprint recognition systems suffer from the threat of presentation attacks due to their wide range of applications in areas including national borders and commercial applications. Presentation attacks can be performed by fabricating the fake fingerprint of a user with or without the intention of the subject. This paper presents a dynamic ensemble of deep learning and handcrafted features to detect presentation attacks in known-material and unknown-material protocols. The proposed model is a dynamic ensemble of deep CNN and handcrafted features empowered deep neural networks both of which learn their parameters together. The proposed presentation attack detection model, in this way, utilizes the capabilities of both classification techniques and exhibits better performance than their individual results. The proposed model's performance is validated using benchmark LivDet 2015, 2017, and 2019 databases, with an overall accuracy of 96.10%, 96.49%, and 95.99% attained on them, respectively. The proposed model outperforms state-of-the-art methods in benchmark protocols of presentation attack detection in terms of classification accuracy.

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“…To locate faces and other objects in a picture, researchers frequently employ the Viola-Jones framework. With open communities like OpenCV, face detection classifiers are shared [25]. With a full solution for image-based face identification and recognition with improved accuracy, a higher response rate, and as a first step towards video surveillance, this system aims to assess face detection and recognition methods.…”

Section: Criminal Identificationmentioning

confidence: 99%

Face and liveness detection with criminal identification using machine learning and image processing techniques for security system

Shinde,

Raundale

2024

IJ-AI

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<p>In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.</p>

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Fingerprint Liveness Detection by a Template-Probe Convolutional Neural Network

Cited by 27 publications

References 17 publications

A lightweight GAN network for large scale fingerprint generation

A lightweight GAN network for large scale fingerprint generation

An open patch generator based fingerprint presentation attack detection using generative adversarial network

Face and liveness detection with criminal identification using machine learning and image processing techniques for security system

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