Static Hand Gesture Recognition Based on Convolutional Neural Networks

This paper proposes a gesture recognition method using convolutional neural networks. The procedure involves the application of morphological filters, contour generation, polygonal approximation, and segmentation during preprocessing, in which they contribute to a better feature extraction. Training...

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Bibliographic Details
Published in:Journal of electrical and computer engineering 2019, Vol.2019 (2019), p.1-12
Main Authors: Paula, Iális C., Almeida, Antônio M. A., Borges, Carlos D. B., Pinto, Raimundo F.
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Deep learning
Feature extraction
Gesture recognition
Methods
Morphological filters
Morphology
Neural networks
Pattern recognition
Principal components analysis
Recognition
Segmentation
Sign language
Signal processing
Skin
Training
Wavelet transforms
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Summary:This paper proposes a gesture recognition method using convolutional neural networks. The procedure involves the application of morphological filters, contour generation, polygonal approximation, and segmentation during preprocessing, in which they contribute to a better feature extraction. Training and testing are performed with different convolutional neural networks, compared with architectures known in the literature and with other known methodologies. All calculated metrics and convergence graphs obtained during training are analyzed and discussed to validate the robustness of the proposed method.
ISSN:2090-0147
2090-0155
DOI:10.1155/2019/4167890