Sequential Belief-Based Fusion of Manual and Non- Manual Signs

This work aims to recognize signs which have both manual and nonmanual components by providing a sequential belief-based fusion mechanism. We propose a methodology based on belief functions for fusing extracted manual and non-manual information in a sequential two-step approach. The belief functions based on the likelihoods of the HMMs are used to decide whether there is an uncertainty in the decision of the first step and the uncertainty clusters. Then, we proceed to the second step, which utilizes only the non-manual features on the identified clusters, only if there is an uncertainty.