Face Detection and Recognizing Object Category in Boosting Framework Using Genetic Algorithms

In this article, the authors have represented the images as a collection of patches, each of which belongs to latent theme that is shared across images as well as categories (1). Various face detection techniques have been proposed over the past decade. Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and do not contribute directly to the face detection algorithm. This creates unnecessary computation and usage of large memory space. In this article, the authors have proposed to enlarge the features search space by enriching it with more types of features. With an additional seven new feature types, they show how Genetic Algorithm (GA) can be used, within the Adaboost framework, to find sets of features which can provide better classifiers with a shorter training time. The technique is referred to as GA Boost for their face detection system.