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• W2004331293 abstract "When estimating age, human experts can provide privileged information that encodes the facial attributes of aging, such as smoothness, face shape, face acne, wrinkles, and bags under-eyes. In automatic age estimation, privileged information is unavailable to test images. To overcome this problem, we hypothesize that asymmetric information can be explored and exploited to improve the generalizability of the trained model. Using the learning using privileged information (LUPI) framework, we tested this hypothesis by carefully defining relative attributes for support vector machine (SVM+) to improve the performance of age estimation. We term this specific setting as relative attribute SVM+ (raSVM+), in which the privileged information enables separation of outliers from inliers at the training stage and effectively manipulates slack variables and age determination errors during model training, and thus guides the trained predictor toward a generalizable solution. Experimentally, the superiority of raSVM+ was confirmed by comparing it with state-of-the-art algorithms on the face and gesture recognition research network (FG-NET) and craniofacial longitudinal morphological face aging databases. raSVM+ is a promising development that improves age estimation, with the mean absolute error reaching 4.07 on FG-NET." @default.
• W2004331293 created "2016-06-24" @default.
• W2004331293 creator A5049900562 @default.
• W2004331293 creator A5054429473 @default.
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• W2004331293 date "2016-03-01" @default.
• W2004331293 modified "2023-10-12" @default.
• W2004331293 title "Relative Attribute SVM+ Learning for Age Estimation" @default.
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• W2004331293 doi "https://doi.org/10.1109/tcyb.2015.2416321" @default.
• W2004331293 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25850101" @default.
• W2004331293 hasPublicationYear "2016" @default.
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