# Predicting Privileged Information for Height Estimation

## TL;DR

This paper introduces a regression-based approach using privileged information to improve human height estimation from anthropometric ratios, outperforming traditional methods in accuracy and speed.

## Contribution

It proposes a novel LUPI framework that predicts privileged anthropometric measurements at test time, enhancing height estimation accuracy.

## Key findings

- Outperforms psilon-SVR+ in accuracy and speed
- Effective across different genders and human quartiles
- Utilizes anthropometric ratios for better feature representation

## Abstract

In this paper, we propose a novel regression-based method for employing privileged information to estimate the height using human metrology. The actual values of the anthropometric measurements are difficult to estimate accurately using state-of-the-art computer vision algorithms. Hence, we use ratios of anthropometric measurements as features. Since many anthropometric measurements are not available at test time in real-life scenarios, we employ a learning using privileged information (LUPI) framework in a regression setup. Instead of using the LUPI paradigm for regression in its original form (i.e., \epsilon-SVR+), we train regression models that predict the privileged information at test time. The predictions are then used, along with observable features, to perform height estimation. Once the height is estimated, a mapping to classes is performed. We demonstrate that the proposed approach can estimate the height better and faster than the \epsilon-SVR+ algorithm and report results for different genders and quartiles of humans.