# Dynamic LIBRAS Gesture Recognition via CNN over Spatiotemporal Matrix Representation

arXiv: https://arxiv.org/abs/2603.25863 · HTML: https://mlsanity.com/paper/2603.25863/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 2 of 2)

- [MediaPipe Hands: On-device Real-time Hand Tracking](https://mlsanity.com/paper/2006.10214/) (2020)
- [Enhancing Brazilian Sign Language Recognition through Skeleton Image Representation](https://mlsanity.com/paper/2404.19148/) (2024)

## Abstract (arXiv)

This paper proposes a method for dynamic hand gesture recognition based on the composition of two models: the MediaPipe Hand Landmarker, responsible for extracting 21 skeletal keypoints of the hand, and a convolutional neural network (CNN) trained to classify gestures from a spatiotemporal matrix representation of dimensions 90 by 21 of those keypoints. The method is applied to the recognition of LIBRAS (Brazilian Sign Language) gestures for device control in a home automation system, covering 11 classes of static and dynamic gestures. For real-time inference, a sliding window with temporal frame triplication is used, enabling continuous recognition without recurrent networks. Tests achieved 95% accuracy under low-light conditions and 92% under normal lighting. The results indicate that the approach is effective, although systematic experiments with greater user diversity are needed for a more thorough evaluation of generalization.