Dynamic LIBRAS Gesture Recognition via CNN over Spatiotemporal Matrix Representation
arXiv cs.AI / 3/30/2026
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Key Points
- The paper introduces a dynamic LIBRAS (Brazilian Sign Language) gesture recognition approach that combines MediaPipe Hand Landmarker for extracting 21 hand skeletal keypoints with a CNN for classification.
- Gestures are encoded as a spatiotemporal matrix of size 90×21 derived from keypoints, allowing the CNN to recognize 11 static and dynamic gesture classes.
- For real-time continuous recognition, the method uses a sliding window and temporal frame triplication to avoid recurrent networks while still capturing temporal context.
- Experiments report 95% accuracy in low-light conditions and 92% accuracy in normal lighting, supporting the feasibility of the approach for home automation device control.
- The authors note that further systematic testing with a wider range of users is needed to better assess generalization performance across diverse populations.
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