깊이 카메라와 SVM을 이용한 수화 인식 시스템

Abstract

In this paper, we propose a sign language recognition system using SVM and depth camera. Especially, we focus on the Korean sign language. For the sign language system, we suggest two methods, one in hand feature extraction stage and the other in recognition stage. Hand features are consisted of the number of fingers, finger length, radius of palm, and direction of the hand. To extract hand features, we use Distance Transform and make hand skeleton. This method is more accurate than a traditional method which uses contours. To recognize hand posture, we develop the decision tree with the hand features. For ∙제1저자 : 김기상 ∙교신저자 : 최형일 ∙투고일 : 2014. 9. 19, 심사일 : 2014. 9. 29, 게재확정일 : 2014. 10 28 * 숭실대학교 미디어학과(School of Media, Soongsil University) ※ 본 연구는 서울시 산학연 협력사업(SS110013)과 2013년도 정부(미래창조과학부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임 (No.2013R1A1A2012012) 64 Journal of The Korea Society of Computer and Information November 2014 more accuracy, we use SVM to determine the threshold value in the decision tree. In the experimental results, we show that the suggested method is more accurate and faster when extracting hand features a recognizing hand postures. ▸