# Using Deep Autoencoders for Facial Expression Recognition

arXiv: https://arxiv.org/abs/1801.08329 · HTML: https://mlsanity.com/paper/1801.08329/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2018.

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

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- [Interactive Multimodal Fusion with Temporal Modeling](https://mlsanity.com/paper/2503.10523/) (2025)
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- [Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging](https://mlsanity.com/paper/2411.10100/) (2024)
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## Abstract (arXiv)

Feature descriptors involved in image processing are generally manually chosen and high dimensional in nature. Selecting the most important features is a very crucial task for systems like facial expression recognition. This paper investigates the performance of deep autoencoders for feature selection and dimension reduction for facial expression recognition on multiple levels of hidden layers. The features extracted from the stacked autoencoder outperformed when compared to other state-of-the-art feature selection and dimension reduction techniques.