# Person Recognition using Facial Micro-Expressions with Deep Learning

arXiv: https://arxiv.org/abs/2306.13907 · HTML: https://mlsanity.com/paper/2306.13907/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 24 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)

## Built on by (most cited first, 3 of 3)

- [Beyond Appearance: Transformer-based Person Identification from Conversational Dynamics](https://mlsanity.com/paper/2510.04753/) (2025)
- [Investigating Identity Signals in Conversational Facial Dynamics via Disentangled Expression Features](https://mlsanity.com/paper/2510.11223/) (2025)
- [Two-Stream Spatial-Temporal Transformer Framework for Person Identification via Natural Conversational Keypoints](https://mlsanity.com/paper/2502.20803/) (2025)

## Abstract (arXiv)

This study investigates the efficacy of facial micro-expressions as a soft biometric for enhancing person recognition, aiming to broaden the understanding of the subject and its potential applications. We propose a deep learning approach designed to capture spatial semantics and motion at a fine temporal resolution. Experiments on three widely-used micro-expression databases demonstrate a notable increase in identification accuracy compared to existing benchmarks, highlighting the potential of integrating facial micro-expressions for improved person recognition across various fields.