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DC poleHodnotaJazyk
dc.contributor.authorHast, Anders
dc.contributor.editorSkala, Václav
dc.date.accessioned2023-10-15T17:10:05Z
dc.date.available2023-10-15T17:10:05Z
dc.date.issued2023
dc.identifier.citationWSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vis 43-52.ion, p.en
dc.identifier.isbn978-80-86943-32-9
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/54398
dc.description.sponsorshipThis work has been partially supported by the Swedish Research Council (Dnr 2020-04652; Dnr 2022-02056) in the projects The City’s Faces. Visual culture and social structure in Stockholm 1880-1930 and The In ternational Centre for Evidence-Based Criminal Law (EB-CRIME). The computations were performed on re sources provided by SNIC through UPPMAX under project SNIC 2021/22-918.en
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectpohlaví a klasifikace pohlavícs
dc.subjectklasifikace podprostoru vloženého prototypucs
dc.subjectsubprostorcs
dc.subjectrozpoznávání obličejůcs
dc.titleSex Classification of Face Images using Embedded Prototype Subspace Classifiersen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedIn recent academic literature Sex and Gender have both become synonyms, even though distinct definitions do exist. This give rise to the question, which of those two are actually face image classifiers identifying? It will be argued and explained why CNN based classifiers will generally identify gender, while feeding face recognition feature vectors into a neural network, will tend to verify sex rather than gender. It is shown for the first time how state of the art Sex Classification can be performed using Embedded Prototype Subspace Classifiers (EPSC) and also how the projection depth can be learned efficiently. The automatic Gender classification, which is produced by the InsightFace project, is used as a baseline and compared to the results given by the EPSC, which takes the feature vectors produced by InsightFace as input. It turns out that the depth of projection needed is much larger for these face feature vectors than for an example classifying on MNIST or similar. Therefore, one important contribution is a simple method to determine the optimal depth for any kind of data. Furthermore, it is shown how the weights in the final layer can be set in order to make the choice of depth stable and independent of the kind of learning data. The resulting EPSC is extremely light weight and yet very accurate, reaching over 98% accuracy for several datasets.en
dc.subject.translatedsex and gender classificationen
dc.subject.translatedsubspacesen
dc.subject.translatedEmbedded Prototype Subspace Classificationen
dc.subject.translatedface recognitionen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3301.7
dc.type.statusPeer-revieweden
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