# Neural Architecture Search Using Genetic Algorithm for Facial Expression Recognition

arXiv: https://arxiv.org/abs/2304.12194 · HTML: https://mlsanity.com/paper/2304.12194/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [Frame attention networks for facial expression recognition in videos](https://mlsanity.com/paper/1907.00193/) (2019)
- [Evolving Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1710.10741/) (2017)
- [Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network](https://mlsanity.com/paper/1902.01019/) (2019)
- [Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges](https://mlsanity.com/paper/2006.05415/) (2020)
- [Learning Vision Transformer with Squeeze and Excitation for Facial Expression Recognition](https://mlsanity.com/paper/2107.03107/) (2021)
- ["Excavating AI" Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset](https://mlsanity.com/paper/2107.13998/) (2021)
- [Coding Facial Expressions with Gabor Wavelets (IVC Special Issue)](https://mlsanity.com/paper/2009.05938/) (2020)
- [Learning Anonymized Representations with Adversarial Neural Networks](https://mlsanity.com/paper/1802.09386/) (2018)
- [Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests](https://mlsanity.com/paper/1607.06250/) (2016)

## Built on by (most cited first, 1 of 1)

- [NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks](https://mlsanity.com/paper/2404.08786/) (2024)

## Abstract (arXiv)

Facial expression is one of the most powerful, natural, and universal signals for human beings to express emotional states and intentions. Thus, it is evident the importance of correct and innovative facial expression recognition (FER) approaches in Artificial Intelligence. The current common practice for FER is to correctly design convolutional neural networks' architectures (CNNs) using human expertise. However, finding a well-performing architecture is often a very tedious and error-prone process for deep learning researchers. Neural architecture search (NAS) is an area of growing interest as demonstrated by the large number of scientific works published in recent years thanks to the impressive results achieved in recent years. We propose a genetic algorithm approach that uses an ingenious encoding-decoding mechanism that allows to automatically evolve CNNs on FER tasks attaining high accuracy classification rates. The experimental results demonstrate that the proposed algorithm achieves the best-known results on the CK+ and FERG datasets as well as competitive results on the JAFFE dataset.