Open AccessHand posture classification with convolutional neural networks on VGG-19 net Architecture
Author(s) -
Syed Athar Bin Amir,
Faturrahman,
Hendra Hendra
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/575/1/012186
Subject(s) - convolutional neural network , artificial intelligence , articulation (sociology) , computer science , net (polyhedron) , pattern recognition (psychology) , deep learning , architecture , computer vision , mathematics , geography , geometry , politics , political science , law , archaeology
This study aims to classify the image depth data Hand Posture. Hand Posture is a form of hand and movement used to communicate. Hand Posture is difficult to classify because various human hand objects are complex articulation objects. The model used in this study is Convolutional Neural Networks using the VGG-19 Net architecture. Based on the results shows an increase in the percentage of classification accuracy in each subject is 0.9976, 1.0, 0.9984, 1.0, and 0.9992 respectively.