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dc.contributor.authorAgarwal, Shivang
dc.contributor.authorRattani, Ajita
dc.contributor.authorChowdary, C. Ravindranath
dc.date.accessioned2021-06-01T03:20:08Z
dc.date.available2021-06-01T03:20:08Z
dc.date.issued2021-07
dc.identifier.citationAgarwal, S., Rattani, A., Chowdary, C. R. (2021). A comparative study on handcrafted features v/s deep features for open-set fingerprint liveness detection, Pattern Recognition Letters, 147, 34-40, https://doi.org/10.1016/j.patrec.2021.03.032.en_US
dc.identifier.issn0167-8655
dc.identifier.issn1872-7344
dc.identifier.urihttps://doi.org/10.1016/j.patrec.2021.03.032
dc.identifier.urihttps://soar.wichita.edu/handle/10057/20066
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractA fingerprint liveness detector is a pattern classifier that is used to distinguish a live finger from a fake (spoof) one in the context of an automated fingerprint recognition system. As liveness detectors or presentation attack detectors are vulnerable to presentation attacks, the security and reliability of fingerprint recognition are compromised. Presentation attack detection mechanisms rely on handcrafted or deep features to classify an image as live or spoof. In addition, to strengthen the security, fingerprint liveness detectors should be robust to presentation attacks fabricated using unknown fabrication materials or fingerprint sensors. In this paper, we conduct a comprehensive study on the impact of handcrafted and deep features from fingerprint images on the classification error rate of the fingerprint liveness detection task. We use LBP, LPQ and BSIF as handcrafted features and VGG-19 and Residual CNN as deep feature extractors for this study. As the problem is targeted as an open-set problem, the emphasis is on achieving better robustness and generalization capability. In our observation, handcrafted features outperformed their deep counterparts in two of the three cases under the within-dataset environment. In the cross-sensor environment, deep features obtained a better accuracy, and in the cross-dataset environment, handcrafted features obtained a lower classification error rate.en_US
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesPattern Recognition Letters;Vol. 147
dc.subjectLiveness detectionen_US
dc.subjectHandcrafted v/s deep featuresen_US
dc.subjectOpen-set classificationen_US
dc.titleA comparative study on handcrafted features v/s deep features for open-set fingerprint liveness detectionen_US
dc.typeArticleen_US
dc.rights.holder© 2021 Elsevier B.V. All rights reserved.en_US


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