abstract
we introduce a novel approach for annotating large quantity of in-the-wild facial
images with high-quality posterior age distribution as labels. each posterior provides a
probability distribution of estimated ages for a face. our approach is motivated by observations that it is easier to distinguish who is the older of two people than to determine
the person’s actual age. given a reference database with samples of known ages and
a dataset to label, we can transfer reliable annotations from the former to the latter via
human-in-the-loop comparisons. we show an effective way to transform such comparisons to posterior via fully-connected and softmax layers, so as to permit end-to-end
training in a deep network. thanks to the efficient and effective annotation approach, we
collect a new large-scale facial age dataset, dubbed ‘megaage’, which consists of 41, 941
images1 . with the dataset, we train a network that jointly performs ordinal hyperplane
classification and posterior distribution learning. our approach achieves state-of-theart results on popular benchmarks such as morph2, adience, and the newly proposed
megaage.
note: there are mistakes in our original paper. please check the appendix for errata.