# I Know How You Feel: Emotion Recognition with Facial Landmarks

arXiv: https://arxiv.org/abs/1805.00326 · HTML: https://mlsanity.com/paper/1805.00326/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2018.

## Built on by (most cited first, 7 of 7)

- [EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning](https://mlsanity.com/paper/2408.11424/) (2024)
- [Regulating Facial Processing Technologies: Tensions Between Legal and Technical Considerations in the Application of Illinois BIPA](https://mlsanity.com/paper/2205.07299/) (2022)
- [Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images](https://mlsanity.com/paper/2201.11197/) (2022)
- [Towards Multi-domain Face Landmark Detection with Synthetic Data from Diffusion model](https://mlsanity.com/paper/2401.13191/) (2024)
- [Emotion Transfer Using Vector-Valued Infinite Task Learning](https://mlsanity.com/paper/2102.05075/) (2021)
- [A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis](https://mlsanity.com/paper/2309.09038/) (2023)
- [Facial Landmark Visualization and Emotion Recognition Through Neural Networks](https://mlsanity.com/paper/2506.17191/) (2025)

## Abstract (arXiv)

Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently proposed methods for emotion recognition solve this task using multi-layered convolutional networks that do not explicitly infer any facial features in the classification phase. In this work, we postulate a fundamentally different approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we extend a recently proposed Deep Alignment Network (DAN), that achieves state-of-the-art results in the recent facial landmark recognition challenge, with a term related to facial features. Thanks to this simple modification, our model called EmotionalDAN is able to outperform state-of-the-art emotion classification methods on two challenging benchmark dataset by up to 5%.