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Ordinal regression aims to classify instances into ordinal categories. Inthis paper, body mass index (BMI) category estimation from facial images iscast as an ordinal regression problem. In particular, noisy binary searchalgorithms based on pairwise comparisons are employed to exploit the ordinalrelationship among BMI categories. Comparisons are performed with Siamesearchitectures, one of which uses the Bradley-Terry model probabilities astarget. The Bradley-Terry model is an approach to describe probabilities of thepossible outcomes when elements of a set are repeatedly compared with oneanother in pairs. Experimental results show that our approach outperformsclassification and regression-based methods at estimating BMI categories.

Ordinal Regression using Noisy Pairwise Comparisons for Body Mass Index Range Estimation