Trajectory-based sign language recognition using Discriminant Analysis in higher-dimensional feature space

Authors:
Wun-Guang Liou; Chung-Yang Hsieh; Wei-Yang Lin
Affiliations:
Dept. of CSIE, Nat. Chung Cheng Univ., Chiayi, Taiwan;Dept. of CSIE, Nat. Chung Cheng Univ., Chiayi, Taiwan;Dept. of CSIE, Nat. Chung Cheng Univ., Chiayi, Taiwan
Venue:
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Year:
2011

Citing 0
Cited 1

Kernel-based representation for 2D/3D motion trajectory retrieval and classification

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a method for recognizing sign language using hand movement trajectory. By applying Kernel Principal Component Analysis (KPCA), the motion trajectory data are firstly mapped into a higher-dimensional feature space for analysis. The advantage of using high dimensionality is that it allows a more flexible decision boundary and thus helps us to achieve better classification accuracy. Then we perform the Nonparametric Discriminant Analysis (NDA) in the higher-dimensional feature space, so that the most helpful information can be extracted. We have tested the proposed method using the Australian Sign Language (ASL) data set. The results demonstrate that our approach outperforms the current state-of-the-art for trajectory-based sign language recognition.