Korean Sign Language Recognition Based on Image and Convolution Neural Network

被引：11

作者：

Shin, Hyojoo ^{[1
]}

Kim, Woo Je ^{[1
]}

Jang, Kyoung-ae ^{[2
]}

机构：

[1] Seoul Natl Univ Sci & Technol, Dept SW Anal & Design, 232 Gongreungro, Seoul, South Korea

[2] Seoul Natl Univ Sci & Technol, Dept Ind & Informat Syst Engn, 232 Gongreungro, Seoul, South Korea

来源：

ICIGP 2019: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON IMAGE AND GRAPHICS PROCESSING / 2019 5TH INTERNATIONAL CONFERENCE ON VIRTUAL REALITY | 2019年

关键词：

Korean Sign Language; Convolution Neural Network; Image; Recognition;

D O I：

10.1145/3313950.3313967

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The purpose of this paper is to develop a convolution neural network based model for Korean sign language recognition. For this purpose, sign language videos were collected for 10 selected words of Korean sign language and these videos were converted into images to have 9 frames. The images with 9 frames were used as input data for the convolution neural network based model developed in this study. In order to develop the model for Korean sign language recognition, experiments for determining the number of convolution layers was first performed. Second, experiments for the pooling which intentionally reduces the features of the feature map was performed. Third, we conducted an experiment to reduce over fitting in the model learning process. Based on the experiments, we have developed a convolution neural network based model for Korean sign language recognition. The accuracy of the developed model was about 84.5% for the 10 selected Korean sign words.

引用

页码：52 / 55

页数：4

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