# Proper Body Landmark Subset Enables More Accurate and 5X Faster Recognition of Isolated Signs in LIBRAS

arXiv: https://arxiv.org/abs/2510.24887 · HTML: https://mlsanity.com/paper/2510.24887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Continuous Sign Language Recognition with Correlation Network](https://mlsanity.com/paper/2303.03202/) (2023)
- [Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action Recognition](https://mlsanity.com/paper/2012.13823/) (2020)
- [Sign Language Video Retrieval with Free-Form Textual Queries](https://mlsanity.com/paper/2201.02495/) (2022)
- [Isolated Sign Language Recognition based on Tree Structure Skeleton Images](https://mlsanity.com/paper/2304.05403/) (2023)
- [Scaling up sign spotting through sign language dictionaries](https://mlsanity.com/paper/2205.04152/) (2022)
- [StepNet: Spatial-temporal Part-aware Network for Isolated Sign Language Recognition](https://mlsanity.com/paper/2212.12857/) (2022)
- [EdGCon: Auto-assigner of Iconicity Ratings Grounded by Lexical Properties to Aid in Generation of Technical Gestures](https://mlsanity.com/paper/2306.01944/) (2023)
- [Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition](https://mlsanity.com/paper/2503.20436/) (2025)
- [Enhancing Brazilian Sign Language Recognition through Skeleton Image Representation](https://mlsanity.com/paper/2404.19148/) (2024)
- [Real-Time Posture Monitoring and Risk Assessment for Manual Lifting Tasks Using MediaPipe and LSTM](https://mlsanity.com/paper/2408.12796/) (2024)
- [TSLFormer: A Lightweight Transformer Model for Turkish Sign Language Recognition Using Skeletal Landmarks](https://mlsanity.com/paper/2505.07890/) (2025)

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

- [The In-Car Sign Language Corpus (ICSL): A Multi-Modal Resource for Constrained-Space Sign Language Recognition](https://mlsanity.com/paper/2607.11341/) (2026)

## Abstract (arXiv)

This paper examines the feasibility of utilizing lightweight body landmark detection for recognizing isolated signs in Brazilian Sign Language (LIBRAS). Although the use of skeleton-image representation has enabled substantial improvements in recognition performance, the use of OpenPose for landmark extraction hindered time performance. In a preliminary investigation, we observed that simply replacing OpenPose with lightweight MediaPipe, while improving processing speed, significantly reduced accuracy. To overcome this limitation, we explored landmark subset selection strategies to optimize recognition performance. Experimental results show that a proper landmark subset achieves comparable or superior performance to state-of-the-art methods while reducing processing time by more than 5X. As an additional contribution, we demonstrate that spline-based imputation effectively mitigates missing landmark issues, leading to substantial accuracy gains.