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dc.contributor.authorBecerra Riera, Fabiola-
dc.contributor.authorMorales González, Annette-
dc.contributor.authorMéndez Vázquez, Heydi-
dc.coverage.spatial7004624en_US
dc.date.accessioned2021-06-30T13:46:10Z-
dc.date.available2021-06-30T13:46:10Z-
dc.date.issued2018-
dc.identifier.citationBecerra-Riera F., Morales-González A., Méndez-Vázquez H. (2018) Exploring Local Deep Representations for Facial Gender Classification in Videos. In: Hernández Heredia Y., Milián Núñez V., Ruiz Shulcloper J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science, vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_12en_US
dc.identifier.urihttps://repositorio.uci.cu/jspui/handle/123456789/9451-
dc.description.abstractGender recognition in videos is a challenging task that has received limited attention in recent years. To tackle this problem, we propose to explore the use of intermediate features of a Convolutional Neural Network (CNN) with a component-based face representation methodology. With this approach we intend to exploit the gender information provided by different face parts. The features extracted from video key frames are combined with two different strategies to preserve the temporal information, and Random Forest classifiers are employed to obtain a final gender prediction for a video sequence. Our results on the McGill and COX datasets show that our proposal outperforms the end-to-end CNN approach and, in the McGill dataset, 100% of accuracy was obtained.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.subjectSOFT-BIOMETRICSen_US
dc.subjectGENDER CLASSIFICATION VIDEO FACE ANALYSISen_US
dc.subjectDEEP LEARNING REPRESENTATIONen_US
dc.titleExploring Local Deep Representations for Facial Gender Classification in Videosen_US
dc.typeconferenceObjecten_US
dc.rights.holderUniversidad de las Ciencias Informáticasen_US
dc.identifier.doihttps://doi.org/10.1007/978-3-030-01132-1_12-
dc.source.initialpage104en_US
dc.source.endpage112en_US
dc.source.titleUCIENCIA 2018en_US
dc.source.conferencetitleUCIENCIAen_US
Aparece en las colecciones: UCIENCIA 2018

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