CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Automatic 2D Hand Tracking in Video Sequences
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Articulated Hand Tracking by PCA-ICA Approach
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
A Unified System for Segmentation and Tracking of Face and Hands in Sign Language Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
A New Approach to Hand Tracking and Gesture Recognition by a New Feature Type and HMM
FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
BRIEF: binary robust independent elementary features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
A real-time multi-cue hand tracking algorithm based on computer vision
VR '10 Proceedings of the 2010 IEEE Virtual Reality Conference
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This paper proposes a robust approach to recognize hand gesture which involves face parts like chin, cheeks, eyes and head in the context of sign language recognition and also attacks the problem of interference between face and hand. ICondensation algorithm is used to track the face, skin color segmentation is applied on face to eliminate eyes, and simple four quadrant multi clue information of face is obtained. Simultaneously two hands are tracked by another ICondensation module and BRIEF feature descriptors are extracted from hand. The multi clue information from four quadrants of face is identified whenever intersection of two tracking modules occurs. This intersection information provides which part of the face is being referred by the hand. Along with BRIEF feature descriptor, face position is used as feature for the gesture recognition. SVM multi-class classifier is used for continuous hand gestures classification. Experimentation is carried out with Indian Sign Language and found that the proposed approach outperforms the other existing methods with the recognition rate of 93.21%.