Earticle

초록

영어

In this paper, we propose a sign language recognition system using an SVM (Support Vector Machine) and a depth camera. In particular, we focus on the Korean sign language. For the sign language system, we suggest two methods, one for the hand feature extraction stage and the other for the recognition stage. Hand features consist of the number of fingers, finger length, palm radius, and hand direction. To extract hand features, we use Distance Transform and a hand skeleton. This method is more accurate and faster than a traditional method that uses contours. To recognize hand posture, we develop a decision tree with hand features. For more accuracy, we use SVM to determine the threshold value in the decision tree. In the experiment results, we show that the suggested method is more accurate and faster when extracting hand features and recognizing hand postures.

목차

Abstract
 1. Introduction
 2. Hand Region Detection using Depth Image
 3. Hand Feature Detection for Recognition
  3.1. Hand Feature Detection using Hand Skeleton Detection
  3.2. Hand Feature Detection using Depth Values
 4. Hand Shape Recognition
 5. Results
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Kisang Kim [ School of Media, Soongsil University, Seoul, Korea ]
• Su-Kyung Kim [ School of Media, Soongsil University, Seoul, Korea ]
• Hyung-Il Choi [ School of Media, Soongsil University, Seoul, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605