# Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1705.07871 · HTML: https://mlsanity.com/paper/1705.07871/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2017.

## Built on by (most cited first, 17 of 17)

- [POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition](https://mlsanity.com/paper/2204.04083/) (2022)
- [Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models](https://mlsanity.com/paper/2304.00600/) (2023)
- [Responsible AI: Gender bias assessment in emotion recognition](https://mlsanity.com/paper/2103.11436/) (2021)
- [An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild](https://mlsanity.com/paper/2107.03465/) (2021)
- [Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion](https://mlsanity.com/paper/2111.10561/) (2021)
- [Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation](https://mlsanity.com/paper/2105.06421/) (2021)
- [Affective Behaviour Analysis via Progressive Learning](https://mlsanity.com/paper/2407.16945/) (2024)
- [Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey](https://mlsanity.com/paper/2412.17616/) (2024)
- [Addressing Racial Bias in Facial Emotion Recognition](https://mlsanity.com/paper/2308.04674/) (2023)
- [An Ensemble Approach for Multiple Emotion Descriptors Estimation Using Multi-task Learning](https://mlsanity.com/paper/2207.10878/) (2022)
- [Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation](https://mlsanity.com/paper/2106.04332/) (2021)
- [Exp-Graph: How Connections Learn Facial Attributes in Graph-based Expression Recognition](https://mlsanity.com/paper/2507.14608/) (2025)
- [MAFER: a Multi-resolution Approach to Facial Expression Recognition](https://mlsanity.com/paper/2105.02481/) (2021)
- [Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News](https://mlsanity.com/paper/2404.13493/) (2024)
- [A Multi-resolution Approach to Expression Recognition in the Wild](https://mlsanity.com/paper/2103.05723/) (2021)
- [7ABAW-Compound Expression Recognition via Curriculum Learning](https://mlsanity.com/paper/2503.07969/) (2025)
- [Multi-modal Transfer Learning for Dynamic Facial Emotion Recognition in the Wild](https://mlsanity.com/paper/2504.21248/) (2025)

## Abstract (arXiv)

Deep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition (FER). In spite of efforts made to improve the accuracy of FER systems using DNN, existing methods still are not generalizable enough in practical applications. This paper proposes a 3D Convolutional Neural Network method for FER in videos. This new network architecture consists of 3D Inception-ResNet layers followed by an LSTM unit that together extracts the spatial relations within facial images as well as the temporal relations between different frames in the video. Facial landmark points are also used as inputs to our network which emphasize on the importance of facial components rather than the facial regions that may not contribute significantly to generating facial expressions. Our proposed method is evaluated using four publicly available databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.