Abstract:
This thesis addresses the problem of vision based sign language recognition and focuses on three main tasks to design improved techniques that increase the perfor- mance of sign language recognition systems. We first attack the markerless tracking problem during natural and unrestricted signing in less restricted environments. We propose a joint particle filter approach for tracking multiple identical objects, in our case the two hands and the face, which is robust to situations including fast move- ment, interactions and occlusions. Our experiments show that the proposed approach has a robust tracking performance during the challenging situations and is suitable for tracking long durations of signing with its ability of fast recovery. Second, we at- tack the problem of the recognition of signs that include both manual (hand gestures) and non-manual (head/body gestures) components. We investigated multi-modal fu- sion techniques to model the different temporal characteristics and propose a two-step sequential belief based fusion strategy. The evaluation of the proposed approach, in comparison to other state of the art fusion approaches, shows that our method models the two modalities better and achieves higher classification rates. Finally, we pro- pose a strategy to combine generative and discriminative models to increase the sign classification accuracy. We apply the Fisher kernel method and propose a multi-class classification strategy for gesture and sign sequences. The results of the experiments show that the classification power of discriminative models and the modeling power of generative models are effectively combined with a suitable multi-class strategy. We also present two applications, a sign language tutor and an automatic sign dictionary, developed based on the ideas and methods presented in this thesis.
