Hand posture classification with convolutional neural networks on VGG-19 net Architecture

Abstract

Abstract 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.

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