A System for Person-Independent Hand Posture Recognition against Complex Backgrounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using marking menus to develop command sets for computer vision based hand gesture interfaces
Proceedings of the second Nordic conference on Human-computer interaction
A Gesture Based Interface for Human-Robot Interaction
Autonomous Robots
Visual motion based behavior learning using hierarchical discriminant regression
Pattern Recognition Letters
Resolving hand over face occlusion
Image and Vision Computing
A hand gesture recognition system based on local linear embedding
Journal of Visual Languages and Computing
Resolving hand over face occlusion
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
An integrated sign language recognition system
Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference
| Hi-index | 0.00 |
In this paper, we have presented a new approach to recognize hand signs. In our approach, motion understanding (the hand movement) is tightly coupled with spatial recognition (hand shape). The system uses the multiclass, multidimensional discriminant analysis to automatically select the most discriminating features for gesture classification. A recursive partition tree approximator is proposed to do classification. This approach combined with our previous work on the hand segmentation forms a new framework which addresses three key aspects of the hand sign interpretation, that is the hand shape, the location, and the movement. The framework has been tested to recognize 28 different hand signs. The experimental results show that the system can achieve a 93.1% recognition rate for test sequences that have not been used in the training phase.