Abstract:
A variety of face detection techniques has been proposed over the past decade. Generally, a large number of features are required to be selected for training purposes. 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 we propose to use Genetic Algorithm (GA) inside the Adaboost framework to select features which provide better cascade of classifiers for face detection with less training time. Eight different feature types are used in GA search compared to only five basic feature types in exhaustive search. These three additional feature types added will enrich the quality of feature solutions but higher computational time is required to train them due to larger search space. To reduce the training time, we use GA to select features during training process of cascade of classifiers. Implementation of this technique to select features during training of cascade of classifiers is done by using Intel OpenCV software. Experiments on set of images from BioID face database showed that by using GA to search on larger number of feature types and sets, we are able to obtain cascade of classifiers for a face detection system with less training time but gives higher detection rates.
