Boosting Framework for Face Recognition

A novel weakness analysis theory has been developed to overcome the limitation of the strong learners in traditional boosting techniques. It is generally believed that boosting-like learning rules are not suited to a strong and stable learner such as LDA. The theory proposed here is composed of a cross-validation mechanism of weakening a strong learner and a subsequent estimation method of appropriate weakness for the classifiers created by the learner. The weakness analysis theory, attempts to boost the strong learner by increasing the diversity between the classifiers created by the learner, at the expense of decreasing their margins, so as to achieve a tradeoff suggested by recent boosting studies for a low generalization error. In addition, a novel distribution accounting for the pair wise class discriminant information is introduced for effective interaction between the booster and the learner. The integration of all these methodologies proposed here leads to a more flexible framework capable of boosting the traditional Face recognizers such as LDA and PCA. Promising experimental results obtained on various difficult face recognition scenarios demonstrate the effectiveness of the proposed approach. We believe that this work is especially beneficial in extending the boosting framework to accommodate general (strong/weak) learners.