Deep learning based static hand gesture recognition
Satybaldina D. Kalymova G.
January 2021Institute of Advanced Engineering and Science
Indonesian Journal of Electrical Engineering and Computer Science
2021#21Issue 1398 - 405 pp.
Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For preprocessing and image segmentation algorithms computer vision methods from the OpenCV and Intel Real Sense libraries are used. The subsystem for features extracting and gestures classification is based on the modified VGG-16 by using the TensorFlow&Keras deep learning framework. Performance of the static gestures recognition system is evaluated using maching learning metrics. Experimental results show that the proposed model, trained on a database of 2000 images, provides high recognition accuracy both at the training and testing stages.
Computer vision , Convolutional neural network , Deep learning , Gesture recognition , Machine learning , VGG-16
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National Research Nuclear University “MEPhI” (Moscow Engineering Physics Institute), Moscow, Russian Federation
Information Technologies Faculty, L.N. Gumilyov Eurasian National University, Nur-Sultan, Kazakhstan
National Research Nuclear University “MEPhI” (Moscow Engineering Physics Institute)
Information Technologies Faculty
10 лет помогаем публиковать статьи Международный издатель
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