# Regularizing Face Verification Nets For Pain Intensity Regression

## TL;DR

This paper introduces a regularized deep learning approach that fine-tunes face verification networks for pain intensity estimation, leveraging large-scale pre-trained features to improve performance on limited datasets.

## Contribution

It proposes a novel regularized regression method for fine-tuning face verification models to estimate pain intensity from facial expressions.

## Key findings

- Achieved state-of-the-art results on UNBC-McMaster dataset.
- Proposed a weighted metric to handle pain intensity imbalance.
- Enhanced pain assessment accuracy with regularized fine-tuning.

## Abstract

Limited labeled data are available for the research of estimating facial expression intensities. For instance, the ability to train deep networks for automated pain assessment is limited by small datasets with labels of patient-reported pain intensities. Fortunately, fine-tuning from a data-extensive pre-trained domain, such as face verification, can alleviate this problem. In this paper, we propose a network that fine-tunes a state-of-the-art face verification network using a regularized regression loss and additional data with expression labels. In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification. The proposed regularized deep regressor is applied to estimate the pain expression intensity and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving the state-of-the-art performance. A weighted evaluation metric is also proposed to address the imbalance issue of different pain intensities.