# Weakly Supervised Continuous Micro-Expression Intensity Estimation Using Temporal Deep Neural Network

arXiv: https://arxiv.org/abs/2512.01145 · HTML: https://mlsanity.com/paper/2512.01145/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 30 Nov 2025.

## Builds on (most cited first, 1 of 1)

- [Objective Micro-Facial Movement Detection Using FACS-Based Regions and Baseline Evaluation](https://mlsanity.com/paper/1612.05038/) (2016)

## Abstract (arXiv)

Micro-facial expressions are brief and involuntary facial movements that reflect genuine emotional states. While most prior work focuses on classifying discrete micro-expression categories, far fewer studies address the continuous evolution of intensity over time. Progress in this direction is limited by the lack of frame-level intensity labels, which makes fully supervised regression impractical. We propose a unified framework for continuous micro-expression intensity estimation using only weak temporal labels (onset, apex, offset). A simple triangular prior converts sparse temporal landmarks into dense pseudo-intensity trajectories, and a lightweight temporal regression model that combines a ResNet18 encoder with a bidirectional GRU predicts frame-wise intensity directly from image sequences. The method requires no frame-level annotation effort and is applied consistently across datasets through a single preprocessing and temporal alignment pipeline. Experiments on SAMM and CASME II show strong temporal agreement with the pseudo-intensity trajectories. On SAMM, the model reaches a Spearman correlation of 0.9014 and a Kendall correlation of 0.7999, outperforming a frame-wise baseline. On CASME II, it achieves up to 0.9116 and 0.8168, respectively, when trained without the apex-ranking term. Ablation studies confirm that temporal modeling and structured pseudo labels are central to capturing the rise-apex-fall dynamics of micro-facial movements. To our knowledge, this is the first unified approach for continuous micro-expression intensity estimation using only sparse temporal annotations.