From members to teams to committee-a robust approach to gestural and multimodal recognition

When building a complex pattern recognizer with high-dimensional input features, a number of selection uncertainties arise. Traditional approaches to resolving these uncertainties typically rely either on the researcher´s intuition or performance evaluation on validation data, both of which result in poor generalization and robustness on test data. This paper describes a novel recognition technique called members to teams to committee (MTC), which is designed to reduce modeling uncertainty. In particular, the MTC posterior estimator is based on a coordinated set of divide-and-conquer estimators that derive from a three-tiered architectural structure corresponding to individual members, teams, and the overall committee. Basically, the MTC recognition decision is determined by the whole empirical posterior distribution, rather than a single estimate. This paper describes the application of the MTC technique to handwritten gesture recognition and multimodal system integration and presents a comprehensive analysis of the characteristics and advantages of the MTC approach.