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Age estimation, age rank, age-rank-bias, groupwise age-ranking, ranking
The task of estimating the age of humans from facial image is a challenging one due to the non-linear and personalized pattern of aging differing from one individual to another. In this work, we investigated the problem of estimating the age of humans from their facial image using a GroupWise age ranking approach complemented by ageing pattern correlation learning. In our proposed GroupWise age-ranking approach, we constructed a reference image set grouped according to ages for each individual in the reference set and used this to obtain age-ranks for each age group in the reference set. The constructed reference set was used to obtain transformed LBP features called age-rank-biased LBP (arLBP) features which were used with attached age-ranks to train an age estimating function for predicting the ages of test images. Our experiments on the publicly available FG-NET dataset and a locally collected dataset (FAGE) shows the best known age estimation accuracy with MAE of 2.34 years on FG-NET using the leave-one-person-out strategy.
Olufade F. W. Onifade, Damilola J. Akinyemi,"GWAgeER – A GroupWise Age Ranking Framework for Human Age Estimation", IJIGSP, vol.7, no.5, pp.1-12, 2015. DOI: 10.5815/ijigsp.2015.05.01
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