TY - GEN
T1 - Real-time sign language recognition in complex background scene based on a hierarchical clustering classification method
AU - Pan, Tse Yu
AU - Lo, Li Yun
AU - Yeh, Chung Wei
AU - Li, Jhe Wei
AU - Liu, Hou Tim
AU - Hu, Min Chun
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/16
Y1 - 2016/8/16
N2 - Cameras are embedded in many mobile/wearable devices and can be used for gesture recognition or even sign language recognition to help the deaf people communicate with others. In this paper, we proposed a vision-based gesture recognition system which can be used in environments with complex background. We design a method to adaptively update the skin color model for different users and various lighting conditions. Three kinds of features are combined to describe the contours and the salient points of hand gestures. Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are integrated to construct a novel hierarchical classification scheme. We evaluated the proposed recognition method on two datasets: (1) the CSL dataset collected by ourselves, in which images were captured in complex background. (2) The public ASL dataset, in which images of the same gesture were captured in different lighting conditions. Our method achieves the accuracies of 99.8% and 94%, respectively, which outperforms the existing works.
AB - Cameras are embedded in many mobile/wearable devices and can be used for gesture recognition or even sign language recognition to help the deaf people communicate with others. In this paper, we proposed a vision-based gesture recognition system which can be used in environments with complex background. We design a method to adaptively update the skin color model for different users and various lighting conditions. Three kinds of features are combined to describe the contours and the salient points of hand gestures. Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are integrated to construct a novel hierarchical classification scheme. We evaluated the proposed recognition method on two datasets: (1) the CSL dataset collected by ourselves, in which images were captured in complex background. (2) The public ASL dataset, in which images of the same gesture were captured in different lighting conditions. Our method achieves the accuracies of 99.8% and 94%, respectively, which outperforms the existing works.
UR - http://www.scopus.com/inward/record.url?scp=84987660635&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84987660635&partnerID=8YFLogxK
U2 - 10.1109/BigMM.2016.44
DO - 10.1109/BigMM.2016.44
M3 - Conference contribution
AN - SCOPUS:84987660635
T3 - Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016
SP - 64
EP - 67
BT - Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Multimedia Big Data, BigMM 2016
Y2 - 20 April 2016 through 22 April 2016
ER -