Improved Hand Tracking and Isolation from Face by ICondensation Multi Clue Algorithm for Continuous Indian Sign Language Recognition

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%.