Transformer with Leveraged Masked Autoencoder for video-based Pain Assessment

TL;DR

This paper introduces a Transformer-based model combined with a Masked Autoencoder to improve facial video analysis for pain assessment, aiming to provide an objective and effective healthcare tool.

Contribution

It presents a novel integration of Masked Autoencoder and Transformer architecture specifically for pain recognition from facial videos.

Findings

Achieved promising results on the AI4Pain dataset.

Enhanced pain level detection accuracy.

Demonstrated effectiveness of combined Autoencoder and Transformer approach.

Abstract

Accurate pain assessment is crucial in healthcare for effective diagnosis and treatment; however, traditional methods relying on self-reporting are inadequate for populations unable to communicate their pain. Cutting-edge AI is promising for supporting clinicians in pain recognition using facial video data. In this paper, we enhance pain recognition by employing facial video analysis within a Transformer-based deep learning model. By combining a powerful Masked Autoencoder with a Transformers-based classifier, our model effectively captures pain level indicators through both expressions and micro-expressions. We conducted our experiment on the AI4Pain dataset, which produced promising results that pave the way for innovative healthcare solutions that are both comprehensive and objective.