# Investigating Identity Signals in Conversational Facial Dynamics via Disentangled Expression Features

arXiv: https://arxiv.org/abs/2510.11223 · HTML: https://mlsanity.com/paper/2510.11223/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Learning an Animatable Detailed 3D Face Model from In-The-Wild Images](https://mlsanity.com/paper/2012.04012/) (2020)
- [EMOCA: Emotion Driven Monocular Face Capture and Animation](https://mlsanity.com/paper/2204.11312/) (2022)
- [Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision](https://mlsanity.com/paper/1905.06817/) (2019)
- [Towards Metrical Reconstruction of Human Faces](https://mlsanity.com/paper/2204.06607/) (2022)
- [Imitator: Personalized Speech-driven 3D Facial Animation](https://mlsanity.com/paper/2301.00023/) (2022)
- [Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos](https://mlsanity.com/paper/2207.11094/) (2022)
- [A Survey on Facial Expression Recognition of Static and Dynamic Emotions](https://mlsanity.com/paper/2408.15777/) (2024)
- [Takin-ADA: Emotion Controllable Audio-Driven Animation with Canonical and Landmark Loss Optimization](https://mlsanity.com/paper/2410.14283/) (2024)
- [Person Recognition using Facial Micro-Expressions with Deep Learning](https://mlsanity.com/paper/2306.13907/) (2023)

## Built on by (most cited first, 1 of 1)

- [Micro-Expression-Aware Avatar Fingerprinting via Inter-Frame Feature Differencing](https://mlsanity.com/paper/2604.23247/) (2026)

## Abstract (arXiv)

This work investigates whether individuals can be identified solely through the pure dynamical components of their facial expressions, independent of static facial appearance. We leverage the FLAME 3D morphable model to achieve explicit disentanglement between facial shape and expression dynamics, extracting frame-by-frame parameters from conversational videos while retaining only expression and jaw coefficients. On the CANDOR dataset of 1,429 speakers in naturalistic conversations, our Conformer model with supervised contrastive learning achieves 61.14%accuracy on 1,429-way classification -- 458 times above chance -- demonstrating that facial dynamics carry strong identity signatures. We introduce a drift-to-noise ratio (DNR) that quantifies the reliability of shape expression separation by measuring across-session shape changes relative to within-session variability. DNR strongly negatively correlates with recognition performance, confirming that unstable shape estimation compromises dynamic identification. Our findings reveal person-specific signatures in conversational facial dynamics, with implications for social perception and clinical assessment.