International Journal of Recent Technology and Engineering (IJRTE)
Various face detection techniques has 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 does not contribute directly to the face detection algorithm. This creates unnecessary computation and usage of large memory space. In this paper, the authors propose 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.