Non-reference image quality assessment for biometric presentation attack detection

Bhogal, Amrit Pal Singh; Söllinger, Dominik; Trung, Pauline; Uhl, Andreas

doi:10.1109/iwbf.2017.7935080

Cited by 19 publications

(12 citation statements)

References 16 publications

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“…From the latter table, we notice that there is a trend of getting best results when combining a larger number of IQM, confirming earlier results in this direction [5]. In order to look into this effect more thoroughly (and to clarify the role of the k-parameter in kNN classification), we have systematically investigated the results of the exhaustive classification scenarios in [6,8]. We found that combining more metrics and choosing k large leads to better results on average, whereas the top results are achieved when using three to six metrics depending on the considered data set.…”

Section: Experiments 1 -Results: Insupporting

confidence: 80%

“…Table 1 shows the best metric combinations in the case of kNN-classification for the considered databases from an exhaustive search. On average, we could improve our results by 7% compared to the single measure results [6,8] and so most of the results are over 90%. From the latter table, we notice that there is a trend of getting best results when combining a larger number of IQM, confirming earlier results in this direction [5].…”

Section: Experiments 1 -Results: Inmentioning

confidence: 77%

“…Complementing earlier results [5], we use (i) a different and larger set of non-reference IQM (six instead of two) and (ii) do not fuse the results with full-reference IQM values but focus on using one or several fused blind IQMs as generic spoofing detection technique. Extending own prior work on using non-reference IQM for presentation attack detection [6,8], (i) we add support vector machine (SVM) as a second classifier, also avoiding datadependent parameter optimisation in its employment and thus achieving better result generalisability and present ISO/IEC 30107-3 compliant evaluation, (ii) we directly train blind/ referenceless image spatial quality evaluator (BRISQUE) NSS on our data instead of using IQM output as classification input features, and (iii) we experimentally evaluate a specific type of open-set classification scenario, where our presentation attack detection schemes are confronted with real sample data of different sensors (i.e. looking into cross-sensor spoofing detection) and fake sample data of unseen subjects.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Non‐reference image quality assessment and natural scene statistics to counter biometric sensor spoofing

Söllinger¹,

Trung²,

Uhl³

2018

IET biom.

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Non-reference image quality measures (IQM) as well as their associated natural scene statistics (NSS) are used to distinguish real biometric data from fake data as used in presentation/sensor spoofing attacks. An experimental study shows that a support vector machine directly trained on NSS as used in blind/referenceless image spatial quality evaluator provides highly accurate classification of real versus fake iris, fingerprint, face, and fingervein data in generic manner. This contrasts to using the IQM directly, the accuracy of which turns out to be rather data set and parameter choice-dependent. While providing very low average classification error rate values for complete training data, generalisation to unseen attack types is difficult in open-set scenarios and obtained accuracy varies in almost unpredictable manner. This implies that for each given sensor/attack setup , the ability of the introduced methods to detect unseen attacks needs to be assessed separately.

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Section: Experiments 1 -Results: Insupporting

confidence: 80%

Section: Experiments 1 -Results: Inmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Non‐reference image quality assessment and natural scene statistics to counter biometric sensor spoofing

Söllinger¹,

Trung²,

Uhl³

2018

IET biom.

Self Cite

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“…Bhogal et al [21] determined six picture quality measures to separate genuine biometric attributes from information as utilized in introduction assaults for order of genuine and fashioned iris. The most ideal decision of picture quality is subject to the dataset.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Binary Hexagonal Extrema Pattern (EBHXEP) Descriptor for Iris Liveness Detection

Agarwal

Jalal

Arya

2020

Wireless Pers Commun

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Biometric traits are frequently used by security agencies for automatic recognition of a person. There are numerous biometric traits used for person identification. In recent years, iris biometric trait becomes very popular and efficient in many security applications. However, biometric systems are prone to presentation attack. This attack is carried out by using spoofing of any biometric modality and present as a genuine trait. The effect of an artificial artifact of a humanoid iris could be in the form of contact lens attack and print attack make difficult the expected policy of a biometric liveness system. In this paper, the different and enhanced feature descriptor has been proposed i.e. Enhanced Binary Hexagonal Extrema Pattern (EBH X EP) for forged iris detection. The relationship between the center pixel and its hexa neighbor has been explored by the suggested descriptor. The Proposed approach is tested on ATVS-FIr DB and IIIT-D CLI database for iris liveness detection and the results show better results for liveness detection in term of accuracy and average error rate.

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“…This is assuming that many image quality metrics are distorted by the display device or paper. Some researchers used individual quality-based features, while others used a combination of quality metrics to better detect spoofing and differentiate between bona-fide and attack iris [12,18,[53][54][55] or face [56][57][58] or both [59,60]. For example, Galbally et al [12] investigated 22 iris-specific quality features and used feature selection to choose the best combination of features that discriminate between live and fake iris images.…”

Section: Image Quality Analysismentioning

confidence: 99%

Deep convolutional neural networks for face and iris presentation attack detection: survey and case study

2020

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Biometric presentation attack detection (PAD) is gaining increasing attention. Users of mobile devices find it more convenient to unlock their smart applications with finger, face, or iris recognition instead of passwords. In this study, the authors survey the approaches presented in the recent literature to detect face and iris presentation attacks. Specifically, they investigate the effectiveness of fine-tuning very deep convolutional neural networks to the task of face and iris antispoofing. They compare two different fine-tuning approaches on six publicly available benchmark datasets. Results show the effectiveness of these deep models in learning discriminative features that can tell apart real from fake biometric images with a very low error rate. Cross-dataset evaluation on face PAD showed better generalisation than state-of-the-art. They also performed cross-dataset testing on iris PAD datasets in terms of equal error rate, which was not reported in the literature before. Additionally, they propose the use of a single deep network trained to detect both face and iris attacks. They have not noticed accuracy degradation compared to networks trained for only one biometric separately. Finally, they analysed the learned features by the network, in correlation with the image frequency components, to justify its prediction decision.

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Non-reference image quality assessment for biometric presentation attack detection

Cited by 19 publications

References 16 publications

Non‐reference image quality assessment and natural scene statistics to counter biometric sensor spoofing

Non‐reference image quality assessment and natural scene statistics to counter biometric sensor spoofing

Enhanced Binary Hexagonal Extrema Pattern (EBHXEP) Descriptor for Iris Liveness Detection

Deep convolutional neural networks for face and iris presentation attack detection: survey and case study

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