# Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity

arXiv: https://arxiv.org/abs/2005.05509 · HTML: https://mlsanity.com/paper/2005.05509/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 8 of 8)

- [EMOCA: Emotion Driven Monocular Face Capture and Animation](https://mlsanity.com/paper/2204.11312/) (2022)
- [Deep Semantic Manipulation of Facial Videos](https://mlsanity.com/paper/2111.07902/) (2021)
- [Are 3D Face Shapes Expressive Enough for Recognising Continuous Emotions and Action Unit Intensities?](https://mlsanity.com/paper/2207.01113/) (2022)
- [CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition](https://mlsanity.com/paper/2208.07221/) (2022)
- [Combining Facial Videos and Biosignals for Stress Estimation During Driving](https://mlsanity.com/paper/2601.04376/) (2026)
- [Data standardization for robust lip sync](https://mlsanity.com/paper/2202.06198/) (2022)
- [On Developing Facial Stress Analysis and Expression Recognition Platform](https://mlsanity.com/paper/2209.07916/) (2022)
- [FIELDS: Face reconstruction with accurate Inference of Expression using Learning with Direct Supervision](https://mlsanity.com/paper/2511.21245/) (2025)

## Abstract (arXiv)

Human emotions analysis has been the focus of many studies, especially in the field of Affective Computing, and is important for many applications, e.g. human-computer intelligent interaction, stress analysis, interactive games, animations, etc. Solutions for automatic emotion analysis have also benefited from the development of deep learning approaches and the availability of vast amount of visual facial data on the internet. This paper proposes a novel method for human emotion recognition from a single RGB image. We construct a large-scale dataset of facial videos (FaceVid), rich in facial dynamics, identities, expressions, appearance and 3D pose variations. We use this dataset to train a deep Convolutional Neural Network for estimating expression parameters of a 3D Morphable Model and combine it with an effective back-end emotion classifier. Our proposed framework runs at 50 frames per second and is capable of robustly estimating parameters of 3D expression variation and accurately recognizing facial expressions from in-the-wild images. We present extensive experimental evaluation that shows that the proposed method outperforms the compared techniques in estimating the 3D expression parameters and achieves state-of-the-art performance in recognising the basic emotions from facial images, as well as recognising stress from facial videos. %compared to the current state of the art in emotion recognition from facial images.