Ordinal Regression using Noisy Pairwise Comparisons for Body Mass Index Range Estimation
Abstract
Ordinal regression aims to classify instances into ordinal categories. In this paper, body mass index (BMI) category estimation from facial images is cast as an ordinal regression problem. In particular, noisy binary search algorithms based on pairwise comparisons are employed to exploit the ordinal relationship among BMI categories. Comparisons are performed with Siamese architectures, one of which uses the Bradley-Terry model probabilities as target. The Bradley-Terry model is an approach to describe probabilities of the possible outcomes when elements of a set are repeatedly compared with one another in pairs. Experimental results show that our approach outperforms classification and regression-based methods at estimating BMI categories.
Cite
@article{arxiv.1811.03268,
title = {Ordinal Regression using Noisy Pairwise Comparisons for Body Mass Index Range Estimation},
author = {Luisa Polania and Dongning Wang and Glenn Fung},
journal= {arXiv preprint arXiv:1811.03268},
year = {2018}
}
Comments
Paper accepted for publication at the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV 2019)