Direct Classification of Emotional Intensity

TL;DR

This paper introduces a novel deep learning model that directly predicts emotional intensity from videos, improving generalization across individuals by using adaptive learning and dynamic emotion information.

Contribution

It presents a new framework for direct emotion intensity classification from videos, bypassing traditional action unit analysis, with adaptive learning for better subject generalization.

Findings

Outperforms existing models in generalization across different people

Achieves accurate intensity scores from 0-10 directly from videos

Demonstrates robustness with adaptive learning techniques

Abstract

In this paper, we present a model that can directly predict emotion intensity score from video inputs, instead of deriving from action units. Using a 3d DNN incorporated with dynamic emotion information, we train a model using videos of different people smiling that outputs an intensity score from 0-10. Each video is labeled framewise using a normalized action-unit based intensity score. Our model then employs an adaptive learning technique to improve performance when dealing with new subjects. Compared to other models, our model excels in generalization between different people as well as provides a new framework to directly classify emotional intensity.