Publication:
Enhancing face pose normalization with deep learning

dc.contributor.authorAnıl ÇELİK
dc.contributor.authorNafiz ARICA
dc.contributor.institutionTanımlanmamış Kurum
dc.contributor.institutionBAHÇEŞEHİR ÜNİVERSİTESİ
dc.date.accessioned2025-09-20T20:00:21Z
dc.date.issued2019
dc.date.submitted20.05.2020
dc.description.abstractIn this study, we propose a hybrid method for face pose normalization, which combines the 3-D modelbased method with stacked denoising autoencoder (SDAE) deep network. Instead of applying a mirroring operationfor the invisible face parts of the posed image, SDAE learns how to fill in those regions by a large set of trainingsamples. In the performance evaluation, we compare the proposed method to four different pose normalization methodsand investigate their effects on facial emotion recognition and verification problems in addition to visual quality tests.Methods evaluated in the experiments include 2-D alignment, 3-D model-based method, pure SDAE-based method, andgenerative adversarial network-based normalization method. Experiments performed on Multi-PIE dataset show thatthe proposed method produces visually reasonable results and outperforms the others in facial emotion recognition. Onthe other hand 2-D alignment is sufficient in the verification problem where the detailed face characteristics should bepreserved in the normalization process.
dc.identifier.doi10.3906/elk-1810-192
dc.identifier.endpage3712
dc.identifier.issn1300-0632
dc.identifier.issn1300-0632
dc.identifier.issue5
dc.identifier.startpage3699
dc.identifier.urihttps://hdl.handle.net/20.500.14719/5302
dc.identifier.volume27
dc.language.isoen
dc.relation.journalTurkish Journal of Electrical Engineering and Computer Sciences
dc.subjectBilgisayar Bilimleri
dc.subjectTeori ve Metotlar
dc.subjectBilgisayar Bilimleri
dc.subjectYapay Zeka
dc.titleEnhancing face pose normalization with deep learning
dc.typeResearch Article
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