FinPAD: State-of-the-art of Fingerprint Presentation Attack Detection Mechanisms, Taxonomy and Future Perspectives

Abstract The fingerprint presentation attack is still a major challenge in biometric systems due to its increased applications worldwide. In the past, researchers used Fingerprint Presentation Attack Detection (FPAD) for user authentication, but it suffers from reliable authentication due to less focus on reducing the ‘error rate’. In this paper, we proposed an algorithm, based on referential image quality (RIQ)-metrics and minutiae count using neural network, k-NN and SVM for FPAD. We evaluate and validate the error rate reduction with different machine learning models on the public domain, such as LivDet crossmatch dataset2015 and achieved an accuracy of 88% with a neural network, 88.6% with k-NN and 88.8% using SVM. In addition, the average classification error (ACE) score is 0.1197 for ANN, 0.1138 for k-NN and 0.1117 for SVM. Thus, the results obtained show that it was achieved a reasonable accuracy with a low ACE score with respect to other state-of-the-art methods.

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

Sensors ◽

10.3390/s18082532 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2532 ◽

Cited By ~ 4

Author(s):

Thi Nguyen ◽

Eunsoo Park ◽

Xuenan Cui ◽

Van Nguyen ◽

Hakil Kim

Keyword(s):

Processing Time ◽

Detection Method ◽

State Of The Art ◽

Network Size ◽

Attack Detection ◽

Gram Matrix ◽

Texture Information ◽

Crucial Problem ◽

Fingerprint Detection ◽

Presentation Attack Detection

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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A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

Multimedia Tools and Applications ◽

10.1007/s11042-021-11235-x ◽

2021 ◽

Author(s):

Choon Beng Tan ◽

Mohd Hanafi Ahmad Hijazi ◽

Norazlina Khamis ◽

Puteri Nor Ellyza binti Nohuddin ◽

Zuraini Zainol ◽

...

Keyword(s):

Recent Work ◽

State Of The Art ◽

Speaker Verification ◽

Attack Detection ◽

Systematic Analysis ◽

Biometric Technology ◽

The Future ◽

Future Direction ◽

Verification Systems ◽

Presentation Attack Detection

AbstractThe emergence of biometric technology provides enhanced security compared to the traditional identification and authentication techniques that were less efficient and secure. Despite the advantages brought by biometric technology, the existing biometric systems such as Automatic Speaker Verification (ASV) systems are weak against presentation attacks. A presentation attack is a spoofing attack launched to subvert an ASV system to gain access to the system. Though numerous Presentation Attack Detection (PAD) systems were reported in the literature, a systematic survey that describes the current state of research and application is unavailable. This paper presents a systematic analysis of the state-of-the-art voice PAD systems to promote further advancement in this area. The objectives of this paper are two folds: (i) to understand the nature of recent work on PAD systems, and (ii) to identify areas that require additional research. From the survey, a taxonomy of voice PAD and the trend analysis of recent work on PAD systems were built and presented, whereby the recent and relevant articles including articles from Interspeech and ICASSP Conferences, mostly indexed by Scopus, published between 2015 and 2021 were considered. A total of 172 articles were surveyed in this work. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established.

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Dual Reweighting Domain Generalization for Face Presentation Attack Detection

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/120 ◽

2021 ◽

Author(s):

Shubao Liu ◽

Ke-Yue Zhang ◽

Taiping Yao ◽

Kekai Sheng ◽

Shouhong Ding ◽

...

Keyword(s):

State Of The Art ◽

Data Distribution ◽

Feature Space ◽

Attack Detection ◽

Relative Importance ◽

Training Process ◽

Large Domain ◽

Face Presentation ◽

Multiple Domains ◽

Presentation Attack Detection

Face anti-spoofing approaches based on domain generalization (DG) have drawn growing attention due to their robustness for unseen scenarios. Previous methods treat each sample from multiple domains indiscriminately during the training process, and endeavor to extract a common feature space to improve the generalization. However, due to complex and biased data distribution, directly treating them equally will corrupt the generalization ability. To settle the issue, we propose a novel Dual Reweighting Domain Generalization (DRDG) framework which iteratively reweights the relative importance between samples to further improve the generalization. Concretely, Sample Reweighting Module is first proposed to identify samples with relatively large domain bias, and reduce their impact on the overall optimization. Afterwards, Feature Reweighting Module is introduced to focus on these samples and extract more domain-irrelevant features via a self-distilling mechanism. Combined with the domain discriminator, the iteration of the two modules promotes the extraction of generalized features. Extensive experiments and visualizations are presented to demonstrate the effectiveness and interpretability of our method against the state-of-the-art competitors.

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