Face Reconstruction from profile to frontal evaluation of face recognition

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dc.authorid0000-0001-6657-9738
dc.contributor.authorAfra, Salim
dc.contributor.authorAlhajj, Reda
dc.date.accessioned2023-02-24T12:30:29Z
dc.date.available2023-02-24T12:30:29Z
dc.date.issued2020
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractOne of the main challenges in face recognition is handling extreme variation of poses which may be faced for images collected in labs and in the wild. Recognizing faces in profile view has been shown to perform poorly compared to using frontal view of faces. Indeed, previous approaches failed to capture distinct features of a profile face compared to a frontal one. Approaches to enhance face recognition on profile faces have been recently proposed following two different trends. One trend depends on training a neural network model with big multi-view face datasets to learn features of faces by handling all poses. The second trend generates a frontal face image (face reconstruction) from any given face pose and applies feature extraction and face recognition on the generated face instead of profile faces. Recent methods for face reconstruction use generative adversarial networks (GAN) learning model to train two competing neural networks to generate authentic frontal view of any pose preserving person’s identity. For the work described in this paper, we trained a feature extraction neural network model to learn representation of any face pose which is then compared with each other using Euclidean distance. We also used two recent face reconstruction techniques to generate frontal faces. We evaluated the performance of using the generated frontal faces against the posed counterparts. In the conducted experiments, we used three face datasets that contain several challenges for face recognition having faces in a variety of poses and in the wild.
dc.identifier.citationAfra, S. ve Alhajj, R. (2020). Face Reconstruction from profile to frontal evaluation of face recognition. Studies in Big Data içinde (117-133. ss.). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32587-9_8
dc.identifier.doi10.1007/978-3-030-32587-9_8
dc.identifier.endpage133
dc.identifier.issn2197-6503
dc.identifier.scopus2-s2.0-85132846106
dc.identifier.scopusqualityQ3
dc.identifier.startpage117
dc.identifier.urihttps://doi.org/10.1007/978-3-030-32587-9_8
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10530
dc.identifier.volume65
dc.indekslendigikaynakScopus
dc.institutionauthorAlhajj, Reda
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofStudies in Big Dataen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFace Frontalization
dc.subjectFace Recognition
dc.subjectFace Reconstruction
dc.subjectPose-Invariant Recognition
dc.titleFace Reconstruction from profile to frontal evaluation of face recognition
dc.typeBook Chapter

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