A new face presentation attack detection method based on face-weighted multi-color multi-level texture features

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dc.authoridTURHAL, UGUR/0000-0002-5627-1833
dc.contributor.authorTurhal, Ugur
dc.contributor.authorYilmaz, Asuman Gunay
dc.contributor.authorNabiyev, Vasif
dc.date.accessioned2024-10-04T18:48:08Z
dc.date.available2024-10-04T18:48:08Z
dc.date.issued2024
dc.departmentBayburt Üniversitesien_US
dc.description.abstractBiometric data (facial, voice, fingerprint, and retinal scans, for example) are widely used in identification due to their unique and irreversible nature. Facial recognition technologies are employed in a wide range of applications due to their contactless nature and convenience. However, technological advancements and the availability of access to personal information have rendered these biometric systems susceptible to attacks utilizing fake faces. As a result, the issue of anti-spoofing has emerged as a critical one in the field of facial recognition. This study proposes a joint face presentation attack (FPA) detection method based on face-weighted multi-color multi-level LBP features extracted from the combination of device-dependent HSV and device-independent L*a*b* color spaces. The facial images were converted to HSV and L*a*b* color spaces. Three levels of regional LBP features were extracted from each color channel and then concatenated. Finally, a Multi-Color Multi-Level LBP (MCML_LBP) feature vector was obtained. In addition, the Face Weighted MCML_LBP feature vector was produced (FW_MCML_LBP) by adding the LBP histogram extracted from the central region of the normalized image. The feature vectors are used to train an SVM classifier after reducing their size using PCA. Twenty-five different test scenarios were subjected to experimentation on the CASIA and Replay-Attack databases. 2.11% EER and 0.19% HTER were achieved on CASIA (Overall) and Replay-Attack (Grandtest) databases, respectively, using the L*a*b color space and the proposed feature extraction method. The results of the study showed that the proposed method was successful in FPA detection compared to the state-of-the-art methods.en_US
dc.identifier.doi10.1007/s00371-023-02866-2
dc.identifier.endpage1552en_US
dc.identifier.issn0178-2789
dc.identifier.issn1432-2315
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85160445732en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1537en_US
dc.identifier.urihttps://doi.org/10.1007/s00371-023-02866-2
dc.identifier.urihttp://hdl.handle.net/20.500.12403/2914
dc.identifier.volume40en_US
dc.identifier.wosWOS:000994983400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofVisual Computeren_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFace recognitionen_US
dc.subjectSpoofingen_US
dc.subjectPresentation attack detectionen_US
dc.subjectColor texture analysisen_US
dc.subjectLocal binary patternen_US
dc.titleA new face presentation attack detection method based on face-weighted multi-color multi-level texture featuresen_US
dc.typeArticleen_US

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