Artificial Intelligence
Automatic Detection of Relevant Head Gestures in American Sign Language Communication
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Robust Real-Time Face Detection
International Journal of Computer Vision
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Hesitation and Conflict: A Belief-Based Approach for Multi-class Problems
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
SignTutor: An Interactive System for Sign Language Tutoring
IEEE MultiMedia
Effect of colorspace transformation, the illuminance component, and color modeling on skin detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Towards automatic annotation of sign language dictionary corpora
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
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This work aims to recognize signs which have both manual and non-manual components by providing a sequential belief-based fusion mechanism. We propose a methodology based on belief functions for fusing extracted manual and non-manual features in a sequential two-step approach. The belief functions based on the likelihoods of the hidden Markov models are used to decide whether there is an uncertainty in the decision of the first step and also to identify the uncertainty clusters. Then we proceed to the second step which utilizes only the non-manual features within the identified cluster, only if there is an uncertainty.