# An optimized Capsule-LSTM model for facial expression recognition with video sequences

arXiv: https://arxiv.org/abs/2106.07564 · HTML: https://mlsanity.com/paper/2106.07564/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 4 of 4)

- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Why do deep convolutional networks generalize so poorly to small image transformations?](https://mlsanity.com/paper/1805.12177/) (2018)
- [Island Loss for Learning Discriminative Features in Facial Expression Recognition](https://mlsanity.com/paper/1710.03144/) (2017)
- [Fine-Grained Facial Expression Analysis Using Dimensional Emotion Model](https://mlsanity.com/paper/1805.01024/) (2018)

## Abstract (arXiv)

To overcome the limitations of convolutional neural network in the process of facial expression recognition, a facial expression recognition model Capsule-LSTM based on video frame sequence is proposed. This model is composed of three networks includingcapsule encoders, capsule decoders and LSTM network. The capsule encoder extracts the spatial information of facial expressions in video frames. Capsule decoder reconstructs the images to optimize the network. LSTM extracts the temporal information between video frames and analyzes the differences in expression changes between frames. The experimental results from the MMI dataset show that the Capsule-LSTM model proposed in this paper can effectively improve the accuracy of video expression recognition.