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dc.contributor.authorSuruliandi, A.
dc.contributor.authorKasthuri, A.
dc.contributor.authorRaja, S. P.
dc.date2021-12
dc.date.accessioned2022-05-09T09:20:44Z
dc.date.available2022-05-09T09:20:44Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/13043
dc.description.abstractFace annotation is a naming procedure that assigns the correct name to a person emerging from an image. Faces that are manually annotated by people in online applications include incorrect labels, giving rise to the issue of label ambiguity. This may lead to mislabelling in face annotation. Consequently, an efficient method is still essential to enhance the reliability of face annotation. Hence, in this work, a novel method named the Similarity Matrix-based Noise Label Refinement (SMNLR) is proposed, which effectively predicts the accurate label from the noisy labelled facial images. To enhance the performance of the proposed method, the deep learning technique named Convolutional Neural Networks (CNN) is used for feature representation. Several experiments are conducted to evaluate the effectiveness of the proposed face annotation method using the LFW, IMFDB and Yahoo datasets. The experimental results clearly illustrate the robustness of the proposed SMNLR method in dealing with noisy labelled faces.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 7, nº 2
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2943es_ES
dc.rightsopenAccesses_ES
dc.subjectconvolutional neural network (CNN)es_ES
dc.subjectdeep learninges_ES
dc.subjectface annotationes_ES
dc.subjectnoise label refinementes_ES
dc.subjectsimilarityes_ES
dc.subjectIJIMAIes_ES
dc.titleDeep Feature Representation and Similarity Matrix based Noise Label Refinement Method for Efficient Face Annotationes_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2021.05.001


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