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In this paper we represent 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 paper we propose to enlarge the features search space by enriching it with more types of features. With an additional seven new feature types, we 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 our face detection system. The GA carries out an evolutionary search over possible features search space which results in a higher number of feature types and sets selected in less time. Experiments on a set of images from BioID database proved that by using GA to search on large number of feature types and sets, GA Boost is able to obtain cascade of boosted classifiers for a face detection system that can give higher detection rates, lower false positive rates and less training time but gives higher detection rates.

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