Abstract
Hand gesture recognition is the process of recognizing meaningful expressions of form and motion by a human involving only the hands. There are plenty of applications where hand gesture recognition can be applied for improving control, accessibility, communication and learning. In the work presented in this paper we conducted experiments with different types of convolutional neural networks, including our own proprietary model. The performance of each model was evaluated on the Marcel dataset providing relevant insight as to how different architectures influence performance. Best results were obtained using the GoogLeNet approach featuring the Inception architecture, followed by our proprietary model and the VGG model.
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Acknowledgments
We would like to acknowledge the support of the European Commission through the project MAESTRA Learning from Massive, Incompletely annotated, and Structured Data (Grant number ICT-2013-612944).
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Strezoski, G., Stojanovski, D., Dimitrovski, I., Madjarov, G. (2018). Hand Gesture Recognition Using Deep Convolutional Neural Networks. In: Stojanov, G., Kulakov, A. (eds) ICT Innovations 2016. ICT Innovations 2016. Advances in Intelligent Systems and Computing, vol 665. Springer, Cham. https://doi.org/10.1007/978-3-319-68855-8_5
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DOI: https://doi.org/10.1007/978-3-319-68855-8_5
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