# Coupling deep and handcrafted features to assess smile genuineness

arXiv: https://arxiv.org/abs/2503.16128 · HTML: https://mlsanity.com/paper/2503.16128/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [RealSmileNet: A Deep End-To-End Network for Spontaneous and Posed Smile Recognition](https://mlsanity.com/paper/2010.03203/) (2020)
- [Less is More: Facial Landmarks can Recognize a Spontaneous Smile](https://mlsanity.com/paper/2210.04240/) (2022)

## Abstract (arXiv)

Assessing smile genuineness from video sequences is a vital topic concerned with recognizing facial expression and linking them with the underlying emotional states. There have been a number of techniques proposed underpinned with handcrafted features, as well as those that rely on deep learning to elaborate the useful features. As both of these approaches have certain benefits and limitations, in this work we propose to combine the features learned by a long short-term memory network with the features handcrafted to capture the dynamics of facial action units. The results of our experiments indicate that the proposed solution is more effective than the baseline techniques and it allows for assessing the smile genuineness from video sequences in real-time.