TY - GEN
T1 - American Sign Language alphabet recognition using Microsoft Kinect
AU - Dong, Cao
AU - Leu, Ming C.
AU - Yin, Zhaozheng
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/19
Y1 - 2015/10/19
N2 - American Sign Language (ASL) alphabet recognition using marker-less vision sensors is a challenging task due to the complexity of ASL alphabet signs, self-occlusion of the hand, and limited resolution of the sensors. This paper describes a new method for ASL alphabet recognition using a low-cost depth camera, which is Microsoft's Kinect. A segmented hand configuration is first obtained by using a depth contrast feature based per-pixel classification algorithm. Then, a hierarchical mode-seeking method is developed and implemented to localize hand joint positions under kinematic constraints. Finally, a Random Forest (RF) classifier is built to recognize ASL signs using the joint angles. To validate the performance of this method, we used a publicly available dataset from Surrey University. The results have shown that our method can achieve above 90% accuracy in recognizing 24 static ASL alphabet signs, which is significantly higher in comparison to the previous benchmarks.
AB - American Sign Language (ASL) alphabet recognition using marker-less vision sensors is a challenging task due to the complexity of ASL alphabet signs, self-occlusion of the hand, and limited resolution of the sensors. This paper describes a new method for ASL alphabet recognition using a low-cost depth camera, which is Microsoft's Kinect. A segmented hand configuration is first obtained by using a depth contrast feature based per-pixel classification algorithm. Then, a hierarchical mode-seeking method is developed and implemented to localize hand joint positions under kinematic constraints. Finally, a Random Forest (RF) classifier is built to recognize ASL signs using the joint angles. To validate the performance of this method, we used a publicly available dataset from Surrey University. The results have shown that our method can achieve above 90% accuracy in recognizing 24 static ASL alphabet signs, which is significantly higher in comparison to the previous benchmarks.
KW - Accuracy
KW - Gesture recognition
KW - Joints
KW - Kinematics
KW - Probability distribution
KW - Thumb
UR - https://www.scopus.com/pages/publications/84951931934
U2 - 10.1109/CVPRW.2015.7301347
DO - 10.1109/CVPRW.2015.7301347
M3 - Conference contribution
AN - SCOPUS:84951931934
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 44
EP - 52
BT - 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015
PB - IEEE Computer Society
T2 - IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015
Y2 - 7 June 2015 through 12 June 2015
ER -