# ExpresSense: Exploring a Standalone Smartphone to Sense Engagement of Users from Facial Expressions Using Acoustic Sensing

arXiv: https://arxiv.org/abs/2301.06762 · HTML: https://mlsanity.com/paper/2301.06762/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2023.

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

- [Cyberoception: Finding a Painlessly-Measurable New Sense in the Cyberworld Towards Emotion-Awareness in Computing](https://mlsanity.com/paper/2504.16378/) (2025)

## Abstract (arXiv)

Facial expressions have been considered a metric reflecting a person's engagement with a task. While the evolution of expression detection methods is consequential, the foundation remains mostly on image processing techniques that suffer from occlusion, ambient light, and privacy concerns. In this paper, we propose ExpresSense, a lightweight application for standalone smartphones that relies on near-ultrasound acoustic signals for detecting users' facial expressions. ExpresSense has been tested on different users in lab-scaled and large-scale studies for both posed as well as natural expressions. By achieving a classification accuracy of ~75% over various basic expressions, we discuss the potential of a standalone smartphone to sense expressions through acoustic sensing.