# Sign Language Recognition Based On Facial Expression and Hand Skeleton

arXiv: https://arxiv.org/abs/2407.02241 · HTML: https://mlsanity.com/paper/2407.02241/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 2 Jul 2024.

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

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Suppressing Uncertainties for Large-Scale Facial Expression Recognition](https://mlsanity.com/paper/2002.10392/) (2020)
- [Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition](https://mlsanity.com/paper/1607.07043/) (2016)
- [A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets](https://mlsanity.com/paper/2204.03328/) (2022)

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

- [Emotion Recognition in Signers](https://mlsanity.com/paper/2512.15376/) (2025)

## Abstract (arXiv)

Sign language is a visual language used by the deaf and dumb community to communicate. However, for most recognition methods based on monocular cameras, the recognition accuracy is low and the robustness is poor. Even if the effect is good on some data, it may perform poorly in other data with different interference due to the inability to extract effective features. To solve these problems, we propose a sign language recognition network that integrates skeleton features of hands and facial expression. Among this, we propose a hand skeleton feature extraction based on coordinate transformation to describe the shape of the hand more accurately. Moreover, by incorporating facial expression information, the accuracy and robustness of sign language recognition are finally improved, which was verified on A Dataset for Argentinian Sign Language and SEU's Chinese Sign Language Recognition Database (SEUCSLRD).