Dynamic LIBRAS Gesture Recognition via CNN over Spatiotemporal Matrix Representation
arXiv AIArchived Mar 30, 2026✓ Full text saved
arXiv:2603.25863v1 Announce Type: cross Abstract: 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 devi
Full text archived locally
✦ AI Summary· Claude Sonnet
Computer Science > Computer Vision and Pattern Recognition
[Submitted on 26 Mar 2026]
Dynamic LIBRAS Gesture Recognition via CNN over Spatiotemporal Matrix Representation
Jasmine Moreira
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.
Comments: 6 pages, 10 figures, 1 table
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2603.25863 [cs.CV]
(or arXiv:2603.25863v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2603.25863
Focus to learn more
Submission history
From: Jasmine Moreira PhD [view email]
[v1] Thu, 26 Mar 2026 19:37:28 UTC (6,355 KB)
Access Paper:
HTML (experimental)
view license
Current browse context:
cs.CV
< prev | next >
new | recent | 2026-03
Change to browse by:
cs
cs.AI
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Export BibTeX Citation
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Demos
Related Papers
About arXivLabs
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)