Genetic Algorithm: An Enhanced Feature Selection Tool for Face Detection

In real time, the face detection is a big task and various techniques have been proposed over the past decade. In general 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 we propose to enlarge the features search space by enriching it with more types of features with the help of Genetic Algorithm (GA) and can be used in real time to select the best feature of the image, with in the Adaboost framework, to provide better classifiers with a shorter training time. 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 very less time. Experiments on a set of images from Bio identification database proved that by using GA to search on large number of feature types and sets, GA technique is able to obtain cascade of classifiers for a face detection system that can give higher detection rates, lower false positive rates and less training time