# SimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild

arXiv: https://arxiv.org/abs/2303.07648 · HTML: https://mlsanity.com/paper/2303.07648/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 68 of its 97 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 30 of 68)

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- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
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- [iBOT: Image BERT Pre-Training with Online Tokenizer](https://mlsanity.com/paper/2111.07832/) (2021)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
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## Abstract (arXiv)

One of the key issues in facial expression recognition in the wild (FER-W) is that curating large-scale labeled facial images is challenging due to the inherent complexity and ambiguity of facial images. Therefore, in this paper, we propose a self-supervised simple facial landmark encoding (SimFLE) method that can learn effective encoding of facial landmarks, which are important features for improving the performance of FER-W, without expensive labels. Specifically, we introduce novel FaceMAE module for this purpose. FaceMAE reconstructs masked facial images with elaborately designed semantic masking. Unlike previous random masking, semantic masking is conducted based on channel information processed in the backbone, so rich semantics of channels can be explored. Additionally, the semantic masking process is fully trainable, enabling FaceMAE to guide the backbone to learn spatial details and contextual properties of fine-grained facial landmarks. Experimental results on several FER-W benchmarks prove that the proposed SimFLE is superior in facial landmark localization and noticeably improved performance compared to the supervised baseline and other self-supervised methods.