Hand gesture recognition using deep convolutional neural networks
Date Issued
2016-09-05
Author(s)
Strezoski, Gjorgji
Stojanovski, Dario
Madjarov, Gjorgji
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.
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.
Subjects
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