Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/234719
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dc.contributor.authorZhenzhen Huen_US
dc.contributor.authorYonggang Wenen_US
dc.contributor.authorJianfeng Wangen_US
dc.contributor.authorMeng Wangen_US
dc.contributor.authorRichang Hongen_US
dc.contributor.authorShuicheng Yanen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:46:51Z-
dc.date.available2020-04-06T08:46:51Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TIP.2016.2633868en_US
dc.identifier.urihttp://localhost/handle/Hannan/234719-
dc.description.abstractAge estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the immensely unlabeled or weakly labeled training data, e.g., the huge amount of human photos in the social networks. These images may provide no age label, but it is easy to derive the age difference for an image pair of the same person. To improve the age estimation accuracy, we propose a novel learning scheme to take advantage of these weakly labeled data through the deep convolutional neural networks. For each image pair, Kullback&x2013;Leibler divergence is employed to embed the age difference information. The entropy loss and the cross entropy loss are adaptively applied on each image to make the distribution exhibit a single peak value. The combination of these losses is designed to drive the neural network to understand the age gradually from only the age difference information. We also contribute a data set, including more than 100 000 face images attached with their taken dates. Each image is both labeled with the timestamp and people identity. Experimental results on two aging face databases show the advantages of the proposed age difference learning system, and the state-of-the-art performance is gained.en_US
dc.format.extent3087,en_US
dc.format.extent3097en_US
dc.publisherIEEEen_US
dc.relation.haspart7762921.pdfen_US
dc.titleFacial Age Estimation With Age Differenceen_US
dc.typeArticleen_US
dc.journal.volume26en_US
dc.journal.issue7en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7762921.pdf2.64 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhenzhen Huen_US
dc.contributor.authorYonggang Wenen_US
dc.contributor.authorJianfeng Wangen_US
dc.contributor.authorMeng Wangen_US
dc.contributor.authorRichang Hongen_US
dc.contributor.authorShuicheng Yanen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:46:51Z-
dc.date.available2020-04-06T08:46:51Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TIP.2016.2633868en_US
dc.identifier.urihttp://localhost/handle/Hannan/234719-
dc.description.abstractAge estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the immensely unlabeled or weakly labeled training data, e.g., the huge amount of human photos in the social networks. These images may provide no age label, but it is easy to derive the age difference for an image pair of the same person. To improve the age estimation accuracy, we propose a novel learning scheme to take advantage of these weakly labeled data through the deep convolutional neural networks. For each image pair, Kullback&x2013;Leibler divergence is employed to embed the age difference information. The entropy loss and the cross entropy loss are adaptively applied on each image to make the distribution exhibit a single peak value. The combination of these losses is designed to drive the neural network to understand the age gradually from only the age difference information. We also contribute a data set, including more than 100 000 face images attached with their taken dates. Each image is both labeled with the timestamp and people identity. Experimental results on two aging face databases show the advantages of the proposed age difference learning system, and the state-of-the-art performance is gained.en_US
dc.format.extent3087,en_US
dc.format.extent3097en_US
dc.publisherIEEEen_US
dc.relation.haspart7762921.pdfen_US
dc.titleFacial Age Estimation With Age Differenceen_US
dc.typeArticleen_US
dc.journal.volume26en_US
dc.journal.issue7en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7762921.pdf2.64 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhenzhen Huen_US
dc.contributor.authorYonggang Wenen_US
dc.contributor.authorJianfeng Wangen_US
dc.contributor.authorMeng Wangen_US
dc.contributor.authorRichang Hongen_US
dc.contributor.authorShuicheng Yanen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T08:46:51Z-
dc.date.available2020-04-06T08:46:51Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TIP.2016.2633868en_US
dc.identifier.urihttp://localhost/handle/Hannan/234719-
dc.description.abstractAge estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the immensely unlabeled or weakly labeled training data, e.g., the huge amount of human photos in the social networks. These images may provide no age label, but it is easy to derive the age difference for an image pair of the same person. To improve the age estimation accuracy, we propose a novel learning scheme to take advantage of these weakly labeled data through the deep convolutional neural networks. For each image pair, Kullback&x2013;Leibler divergence is employed to embed the age difference information. The entropy loss and the cross entropy loss are adaptively applied on each image to make the distribution exhibit a single peak value. The combination of these losses is designed to drive the neural network to understand the age gradually from only the age difference information. We also contribute a data set, including more than 100 000 face images attached with their taken dates. Each image is both labeled with the timestamp and people identity. Experimental results on two aging face databases show the advantages of the proposed age difference learning system, and the state-of-the-art performance is gained.en_US
dc.format.extent3087,en_US
dc.format.extent3097en_US
dc.publisherIEEEen_US
dc.relation.haspart7762921.pdfen_US
dc.titleFacial Age Estimation With Age Differenceen_US
dc.typeArticleen_US
dc.journal.volume26en_US
dc.journal.issue7en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7762921.pdf2.64 MBAdobe PDF