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

Luisa F. Polanía, Glenn Fung, Dongning Wang

WACV 2019 pp. 782-790

doi:10.1109/WACV.2019.00088 /wacv/2019/polania2019wacv-ordinal/

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 describes 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.

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Cite

Text

Polanía et al. "Ordinal Regression Using Noisy Pairwise Comparisons for Body Mass Index Range Estimation." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019. doi:10.1109/WACV.2019.00088

Markdown

[Polanía et al. "Ordinal Regression Using Noisy Pairwise Comparisons for Body Mass Index Range Estimation." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019.](https://mlanthology.org/wacv/2019/polania2019wacv-ordinal/) doi:10.1109/WACV.2019.00088

BibTeX

@inproceedings{polania2019wacv-ordinal,
  title     = {{Ordinal Regression Using Noisy Pairwise Comparisons for Body Mass Index Range Estimation}},
  author    = {Polanía, Luisa F. and Fung, Glenn and Wang, Dongning},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2019},
  pages     = {782-790},
  doi       = {10.1109/WACV.2019.00088},
  url       = {https://mlanthology.org/wacv/2019/polania2019wacv-ordinal/}
}