Consensus and stacking based fusion and survey of facial feature point detectors

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dc.authoridUlukaya, Sezer/0000-0003-0473-7547
dc.authoridEroglu Erdem, Cigdem/0000-0002-9264-5652
dc.authorwosidUlukaya, Sezer/HJY-5331-2023
dc.authorwosidUlukaya, Sezer/N-9772-2015
dc.authorwosidEroglu Erdem, Cigdem/Z-4276-2019
dc.contributor.authorUlukaya, Sezer
dc.contributor.authorSandikci, Esra Nur
dc.contributor.authorErdem, Cigdem Eroglu
dc.date.accessioned2024-06-12T11:01:46Z
dc.date.available2024-06-12T11:01:46Z
dc.date.issued2022
dc.departmentTrakya Üniversitesien_US
dc.description.abstractFacial landmark detection is a crucial pre-processing step for many applications including face tracking, face recognition and facial affect recognition. Hence, we first aim to investigate and experimentally compare the most successful open source facial feature point detection algorithms published in the last decade. We first present an overview of surveys on facial feature detection algorithms to provide insight into the challenges and innovations. We also propose a consensus-based selection and stacked regression based fusion of facial landmark methods to combine their results in order to achieve superior accuracy. Five open-source algorithms in the literature are objectively compared using the same test data and regression based models have been shown to be more successful. According to the extensive experimental results, the proposed consensus and stacking based fusion method gives the lowest facial landmark detection error as compared to the five most successful algorithms in the literature. Consensus and stacking based fusion of an ensemble of methods boosts the performance of facial landmark detection. The proposed fusion method can also be applied future methods as they emerge.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [116E088]en_US
dc.description.sponsorshipThis work has been supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under the project number: 116E088.en_US
dc.identifier.doi10.1007/s12652-021-03662-3
dc.identifier.issn1868-5137
dc.identifier.issn1868-5145
dc.identifier.scopus2-s2.0-85122877362en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s12652-021-03662-3
dc.identifier.urihttps://hdl.handle.net/20.500.14551/21005
dc.identifier.wosWOS:000741617500002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofJournal Of Ambient Intelligence And Humanized Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFacial Biometricsen_US
dc.subjectFacial Landmark Detectionen_US
dc.subjectFacial Feature Localizationen_US
dc.subjectFusionen_US
dc.subjectFace Alignmenten_US
dc.titleConsensus and stacking based fusion and survey of facial feature point detectorsen_US
dc.typeArticleen_US

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