Genetic algorithms-based dominant feature selection for face detection application

A key challenge in computer vision applications is detecting objects in an image which is a non-trivial problem. One of the better performing proposed algorithms falls within the Viola and Jones framework. They make use of Adaboost for training a cascade of classifiers. The challenges of Adaboost-based face detector include the selection of the most relevant features which are considered as weak classifiers. However, selection of features based on lowering classification error leads to high computation complexity. To overcome this limitation, a novel genetic Adaboost is proposed in our work. In the same context of optimisation, a selection method based on Pareto concept of the most relevant features referred to as dominant features is proposed. This optimisation allows to reduce the initial feature space by 28%. Moreover, we notice that dominant features with genetic Adaboost further improve the performance of genetic Adaboost, reducing the total number of features by 20%.