# Eyemotion: Classifying facial expressions in VR using eye-tracking cameras

arXiv: https://arxiv.org/abs/1707.07204 · HTML: https://mlsanity.com/paper/1707.07204/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2017.

## Built on by (most cited first, 12 of 12)

- [Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion](https://mlsanity.com/paper/2111.10561/) (2021)
- [Robust Egocentric Photo-realistic Facial Expression Transfer for Virtual Reality](https://mlsanity.com/paper/2104.04794/) (2021)
- [InMyFace: Inertial and Mechanomyography-Based Sensor Fusion for Wearable Facial Activity Recognition](https://mlsanity.com/paper/2302.04024/) (2023)
- [In the Blink of an Eye: Event-based Emotion Recognition](https://mlsanity.com/paper/2310.04043/) (2023)
- [Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition](https://mlsanity.com/paper/2407.09521/) (2024)
- [Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices](https://mlsanity.com/paper/2201.09933/) (2022)
- [Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition](https://mlsanity.com/paper/2204.12624/) (2022)
- [Facial Emotion Recognition in VR Games](https://mlsanity.com/paper/2312.06925/) (2023)
- [DriveGazen: Event-Based Driving Status Recognition using Conventional Camera](https://mlsanity.com/paper/2412.11753/) (2024)
- [Multi-modal Collaborative Optimization and Expansion Network for Event-assisted Single-eye Expression Recognition](https://mlsanity.com/paper/2505.12007/) (2025)
- [Enhancing Facial Expression Recognition in Head-Mounted Displays with Synthetic Data](https://mlsanity.com/paper/2607.04490/) (2026)
- [Occlusion-Robust Multimodal Emotion Recognition in VR via Fusion of Facial Images and EMG](https://mlsanity.com/paper/2609.03569/) (2026)

## Abstract (arXiv)

One of the main challenges of social interaction in virtual reality settings is that head-mounted displays occlude a large portion of the face, blocking facial expressions and thereby restricting social engagement cues among users. Hence, auxiliary means of sensing and conveying these expressions are needed. We present an algorithm to automatically infer expressions by analyzing only a partially occluded face while the user is engaged in a virtual reality experience. Specifically, we show that images of the user's eyes captured from an IR gaze-tracking camera within a VR headset are sufficient to infer a select subset of facial expressions without the use of any fixed external camera. Using these inferences, we can generate dynamic avatars in real-time which function as an expressive surrogate for the user. We propose a novel data collection pipeline as well as a novel approach for increasing CNN accuracy via personalization. Our results show a mean accuracy of 74% (F1 of 0.73) among 5 `emotive' expressions and a mean accuracy of 70% (F1 of 0.68) among 10 distinct facial action units, outperforming human raters.