Abstract
In this paper, we investigate the idea of using Artificial Neural Network (ANN) and Genetic Algorithms (GAs) to detect upright, frontal views of faces in complex gray-scale scenes. The first component of our system is a neural network that receives as input a 20x20 pixel region of the image, and generates an output ranging from 0 to 1, signing the presence or absence of a face. The neural network is trained with many face examples to learn the concept of face. GA is employed to search efficiently the input image by extracting sub-windows from the input image. The sub-window is sent to the NN, what is called the face filter, to see how closely it resembles a face. Fitness is given according to the output of the NN. Based on its fitness the sub-window is retained by the GA in subsequent steps. Experimental results show that significant speedup can be achieved by applying the proposed approach.
