Facial age estimation based on advanced ordinal ranking

Ordinal hyperplane ranking achieves superior performance in facial age estimation. However, further experiments show that this approach suffers from its ideal ranking rule, which sometimes causes unnecessary estimation deviations and degrades performance. Two approaches with new ranking rules are proposed, which minimise accidental deviations of binary classifiers and tactfully combine the accuracy and obtained label in each binary classification substep for the ranking criteria. Moreover, at first the extreme learning machine is introduced into facial age estimation, taking full advantage of its high learning speed and accuracy. Experimental results from public datasets are presented to demonstrate that the proposed algorithms reduce the mean absolute error and improve age estimation performance while reducing runtime significantly.