TY - GEN
T1 - Low complexity classification system for glove-based arabic sign language recognition
AU - Assaleh, Khaled
AU - Shanableh, Tamer
AU - Zourob, Mohammed
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Accumulated differences
KW - Arabic sign language recognition
KW - Dynamic time warping
KW - Sensor gloves
UR - https://www.scopus.com/pages/publications/84869014936
U2 - 10.1007/978-3-642-34487-9_32
DO - 10.1007/978-3-642-34487-9_32
M3 - Conference contribution
AN - SCOPUS:84869014936
SN - 9783642344862
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 262
EP - 268
BT - Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
T2 - 19th International Conference on Neural Information Processing, ICONIP 2012
Y2 - 12 November 2012 through 15 November 2012
ER -