Publication:
Soft biometrics: Gender recognition from unconstrained face images using local feature descriptor

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dc.citedby9
dc.contributor.authorArigbabu O.A.en_US
dc.contributor.authorAhmad S.M.S.en_US
dc.contributor.authorAdnan W.A.W.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorMahmood S.en_US
dc.contributor.authorid56047591000en_US
dc.contributor.authorid24721182400en_US
dc.contributor.authorid6506665562en_US
dc.contributor.authorid16023225600en_US
dc.contributor.authorid56606751300en_US
dc.date.accessioned2023-05-29T06:01:57Z
dc.date.available2023-05-29T06:01:57Z
dc.date.issued2015
dc.description.abstractGender recognition from unconstrained face images is a challenging task due to the high degree of misalignment, pose, expression, and illumination variation. In previous works, the recognition of gender from unconstrained face images is approached by utilizing image alignment, exploiting multiple samples per individual to improve the learning ability of the classifi er, or learning gender based on prior knowledge about pose and demographic distributions of the dataset. However, image alignment increases the complexity and time of computation, while the use of multiple samples or having prior knowledge about data distribution is unrealistic in practical applications. This paper presents an approach for gender recognition from unconstrained face images. Our technique exploits the robustness of local feature descriptor to photometric variations to extract the shape description of the 2D face image using a single sample image per individual. The results obtained from experiments on Labeled Faces in the Wild (LFW) dataset describe the effectiveness of the proposed method. The essence of this study is to investigate the most suitable functions and parameter settings for recognizing gender from unconstrained face images.en_US
dc.description.natureFinalen_US
dc.identifier.epage122
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84928542018
dc.identifier.spage111
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84928542018&partnerID=40&md5=70e7d2b9768fde58099c409dcf4d51d1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22554
dc.identifier.volume14
dc.publisherUniversiti Utara Malaysia Pressen_US
dc.sourceScopus
dc.sourcetitleJournal of Information and Communication Technology
dc.titleSoft biometrics: Gender recognition from unconstrained face images using local feature descriptoren_US
dc.typeArticleen_US
dspace.entity.typePublication
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