Ordinal Hyperplanes Ranker with Cost Sensitivities for Age Estimation
In this paper, we propose an ordinal hyperplane ranking algorithm called OHRank, which estimates human
ages via facial images. The design of the algorithm is based on the relative order information among the
age labels in a database. Each ordinal hyperplane separates all the facial images into two groups according
to the relative order, and a cost-sensitive property is exploited to find better hyperplanes based on the
classification costs. Human ages are inferred by aggregating a set of preferences from the ordinal
hyperplanes with their cost sensitivities. Our experimental results demonstrate that the proposed approach
outperforms conventional multiclass-based and regression-based approaches as well as recently developed
ranking-based age estimation approaches.