DeFraudNet:End2End Fingerprint Spoof Detection using Patch Level Attention

Anusha, B.; Banerjee, Sayan; Chaudhuri, Subhasis

doi:10.1109/wacv45572.2020.9093397

Cited by 10 publications

(2 citation statements)

References 27 publications

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“…Agarwal and Chowdary [139] proposed the utilization of stacking and bagging ensemble learning approaches in fingerprint PAD. The suggested algorithms considered the similarities of datasets utilized for PAD.…”

Section: Ref Year Dataset Feature Extraction Resultsmentioning

confidence: 99%

A comprehensive survey of fingerprint presentation attack detection

Κarampidis¹,

Rousouliotis²,

Linardos³

et al. 2021

JSSS

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Section: Ref Year Dataset Feature Extraction Resultsmentioning

confidence: 99%

A comprehensive survey of fingerprint presentation attack detection

Κarampidis¹,

Rousouliotis²,

Linardos³

et al. 2021

JSSS

View full text Add to dashboard Cite

“…Recently, there have been some attempts by researchers to address the poor generalization abilities of spoof detectors. In [19], the authors consider the poor generalization ability of the spoof detectors and propose an end-to-end patch level network to address the issue. In [20] and [5], spoof fingerprint generalization approaches based on CNN architecture are proposed.…”

Section: Related Workmentioning

confidence: 99%

EaZy Learning: An Adaptive Variant of Ensemble Learning for Fingerprint Liveness Detection

Agarwal¹,

Chowdary²,

Sourabh³

2021

Preprint

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In the field of biometrics, fingerprint recognition systems are vulnerable to presentation attacks made by artificially generated spoof fingerprints. Therefore, it is essential to perform liveness detection of a fingerprint before authenticating it. Fingerprint liveness detection mechanisms perform well under the within-dataset environment but fail miserably under cross-sensor (when tested on a fingerprint acquired by a new sensor) and cross-dataset (when trained on one dataset and tested on another) settings. To enhance the generalization abilities, robustness and the interoperability of the fingerprint spoof detectors, the learning models need to be adaptive towards the data. We propose a generic model, EaZy learning which can be considered as an adaptive midway between eager and lazy learning. We show the usefulness of this adaptivity under crosssensor and cross-dataset environments. EaZy learning examines the properties intrinsic to the dataset while generating a pool of hypotheses. EaZy learning is similar to ensemble learning as it generates an ensemble of base classifiers and integrates them to make a prediction. Still, it differs in the way it generates the base classifiers. EaZy learning develops an ensemble of entirely disjoint base classifiers which has a beneficial influence on the diversity of the underlying ensemble. Also, it integrates the predictions made by these base classifiers based on their performance on the validation data. Experiments conducted on the standard high dimensional datasets LivDet 2011, LivDet 2013 and LivDet 2015 prove the efficacy of the model under cross-dataset and cross-sensor environments.

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