Abstract
The paper describes a depth-based hand pose recognizer by means of a Learning Vector Quantization (LVQ) classifier. The hand pose recognizer is composed of three modules. The first module segments the scene isolating the hand. The second one carries out the feature extraction, representing the hand by a set of 8 features. The third module, the classifier, is a LVQ. The recognizer, tested on a dataset of 6500 hand poses, carried out by people of different sex and physical aspect, has shown an accuracy larger than 99% recognition rate. The hand pose recognizer accuracy is among highest presented in literature for hand pose recognition.
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Notes
- 1.
The convex hull of an image is the minimum polygon enclosing the image itself.
- 2.
A convexity defect is a point of the image contour where the contour is not convex anymore.
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Acknowledgements
Domenico De Felice developed part of the work, during his M.Sc. thesis in Computer Science at University of Naples Parthenope, with the supervision of Francesco Camastra. The research was funded by Sostegno alla ricerca individuale per il triennio 2015–17 project of University of Naples Parthenope.
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De Felice, D., Camastra, F. (2018). Depth-Based Hand Pose Recognizer Using Learning Vector Quantization. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_7
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DOI: https://doi.org/10.1007/978-3-319-56904-8_7
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