Knowledge Sharing and Cooperation Based Adaptive Boosting for Robust Eye Detection
Avisek Lahiri, Prabir Kumar Biswas · 2014
This paper proposes knowledge sharing and cooperation based Adaptive Boosting (KSC-AdaBoost) for supervised collaborative learning in presence of two different feature spaces (views) representing a training example. In such a binary learner space, two learner agents are trained on the two feature spaces. Difficulty of a training example is ascertained not only by classification performance of an individual learner but also by overall group performance on that training example. Group learning is enhanced by a novel algorithm for assigning weight to training set data. Three different models of KSC-AdaBoost are proposed for agglomerating decisions of the two learners. KSC-AdaBoost outperforms traditional AdaBoost and some recent variants of AdaBoost in terms of convergence rate of training set error and generalization accuracy. The paper then presents KSC-AdaBoost based hierarchical model for accurate eye region localization followed by fuzzy rule based system for robust eye center detection. Exhaustive experiments on five publicly available popular datasets reveal the viability of the learning models and superior eye detection accuracy over recent state-of-the-art algorithms.