A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint

Bhardwaj, Ishan; Londhe, Narendra D.; Kopparapu, Sunil Kumar

doi:10.1016/j.patcog.2016.09.003

Cited by 30 publications

(16 citation statements)

References 40 publications

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“…NIPS) were not included due to their primary focus on algorithms and lack of attention to system applications. (2) In order to evaluate current practices, the paper had to be published within the last 2 years (2016 to 2018). (3) The paper had to propose an authentication scheme.…”

Section: Review Of Recent Authentication Systemsmentioning

confidence: 99%

“…(3) The paper had to propose an authentication scheme. Specifically, the paper had to use machine learning to label [12], [33], [41], [42] IMWUT/UbiComp [11], [20], [25], [35], [48] INFOCOM [8], [36], [45], [51], [53] MobiCom [15], [32] MobiSys [7], [31] NDSS [5], [17], [49], [50] Pattern Recognition [2], [9], [18], [24], [38], [40], [54]…”

Section: Review Of Recent Authentication Systemsmentioning

confidence: 99%

“…EER % ACC % FPR % 0.00 [9] 99.30 [55] 0.00 [11] 0.34 [24] 98.61 [32] 0.01 [9] 0.59 [2] 98.47 [51] 0.10 [35] 0.95 [40] 98.00 [53] 0.10 [5] 1.26 [55] 97.00 [42] 0.10 [40] TABLE III: The top five authentication systems according to a naïve comparison of their best reported values for EER, ACC, and FPR metrics. These metrics are reported the most often but rarely yield a meaningful comparison.…”

Section: B a Naïve Comparisonmentioning

confidence: 99%

See 2 more Smart Citations

Robust Performance Metrics for Authentication Systems

Sugrim

Liu

McLean³

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

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Research has produced many types of authentication systems that use machine learning. However, there is no consistent approach for reporting performance metrics and the reported metrics are inadequate. In this work, we show that several of the common metrics used for reporting performance, such as maximum accuracy (ACC), equal error rate (EER) and area under the ROC curve (AUROC), are inherently flawed. These common metrics hide the details of the inherent tradeoffs a system must make when implemented. Our findings show that current metrics give no insight into how system performance degrades outside the ideal conditions in which they were designed. We argue that adequate performance reporting must be provided to enable meaningful evaluation and that current, commonly used approaches fail in this regard. We present the unnormalized frequency count of scores (FCS) to demonstrate the mathematical underpinnings that lead to these failures and show how they can be avoided. The FCS can be used to augment the performance reporting to enable comparison across systems in a visual way. When reported with the Receiver Operating Characteristics curve (ROC), these two metrics provide a solution to the limitations of currently reported metrics. Finally, we show how to use the FCS and ROC metrics to evaluate and compare different authentication systems.

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Section: Review Of Recent Authentication Systemsmentioning

confidence: 99%

Section: Review Of Recent Authentication Systemsmentioning

confidence: 99%

Section: B a Naïve Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

Robust Performance Metrics for Authentication Systems

Sugrim

Liu

McLean³

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

View full text Add to dashboard Cite

show abstract

“…Recently, a new group of fingerprint PAD methods have also been considered, which fall outside of software-and hardware-based approaches and are based on the fusion of fingerprint with a more secure biometric modality [30,31]. Several researchers investigated the fusion of fingerprints with a variety of biometric modalities, such as face, ECG, and fingerprint dynamics, to improve the accuracy and security of biometric systems [19,[32][33][34][35][36][37][38][39][40][41]. The fusion of an ECG with other biometric modalities [37,38,[42][43][44][45][46] has also received attention because the ECG has certain biometric advantages, such as a natural inherence of the liveness characteristic and a continuous authentication over time [47].…”

Section: Introductionmentioning

confidence: 99%

End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection

Jomaa

Mathkour

Bazi

et al. 2020

Sensors

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Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.

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“…Nevertheless, neither one of hardwarebased and software-based liveness detection systems have shown acceptable performance and cost against spoof attacks. Physiological and behavioral characteristics are also employed to enhance multimodal verification security in [7]. This paper is enlightened by the fact that in a partial spoof attack, the recognition scores achieved from non-spoofed modalities are generally near the imposter score distribution center, given that they are also zero effort attempts from a unimodal viewpoint.…”

Section: Introductionmentioning

confidence: 99%

Improve the Spoofing Resistance of Multimodal Verification with Representation-Based Measures

Huang

Feng

Kittler

et al. 2018

Pattern Recognition and Computer Vision

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Recently, the security of multimodal verification has become a growing concern since many fusion systems have been known to be easily deceived by partial spoof attacks, i.e. only a subset of modalities is spoofed. In this paper, we verify such a vulnerability and propose to use two representation-based metrics to close this gap. Firstly, we use the collaborative representation fidelity with non-target subjects to measure the affinity of a query sample to the claimed client. We further consider sparse coding as a competing comparison among the client and the non-target subjects, and hence explore two sparsity-based measures for recognition. Last, we select the representation-based measure, and assemble its score and the affinity score of each modality to train a support vector machine classifier. Our experimental results on a chimeric multimodal database with face and ear traits demonstrate that in both regular verification and partial spoof attacks, the proposed method significantly outperforms the well-known fusion methods with conventional measure.

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A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint

Cited by 30 publications

References 40 publications

Robust Performance Metrics for Authentication Systems

Robust Performance Metrics for Authentication Systems

End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection

Improve the Spoofing Resistance of Multimodal Verification with Representation-Based Measures

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