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Low complexity classification system for glove-based arabic sign language recognition
, Shanableh T., Zourob M.
Published in Springer
Volume: 7665 LNCS
Issue: PART 3
Pages: 262 - 268
This paper presents a low complexity classification approach for sign language recognition using sensor-based gloves. Each glove includes 5 bend sensors and a 3D accelerometer. The classification approach is based on a novel feature extraction method based on accumulated differences (ADs). The ADs approach projects the dynamics of the glove sensor readings into one feature vector. This vector is normally of high dimensionality as it is meant to capture the dynamics of a sign language gesture. As such, dimensionality reduction using stepwise regression is applied to feature vectors before classification. Thereafter, a simple minimum distance classifier is employed. The proposed system is applied to a dataset Arabic sign language gestures and it yielded a recognition rates 92.5% and 95.1% for user dependent and user independent models respectively. Moreover, the computational complexity of the proposed method is O(N) as compared to the classical approach of Dynamic Time Warping (DTW) which is of O(N 2) complexity. © 2012 Springer-Verlag.
About the journal
JournalData powered by TypesetInternational Conference on Neural Information Processing
PublisherData powered by TypesetSpringer
Open AccessNo