Use of experiment design methods to determine unseen data in face recognition problems

Abstract

Face detection is the first step in any face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system for identification. This project will use general preprocessing approach for normalizing the image and Radial Basis Function (RBF) Neural Networks will be used for distinguishing face and non-face images. Face and non-face data will be used for training the RBF network in order for the network to discriminate face and non-face images. The non-face data were normally taken randomly from the internet or subtracted from scenery images. Creating these non-face images is tedious especially when thousands of data needed. Experiment design approach are investigated to solve this problem where the non-face images are computer generated. However, this approach was found to be unfeasible due to the long computational time to produce one single non-face image even though a high performance computer was used. The second focus of this project is to design a novel RBF neural network algorithm that can detect non-face images effectively from a given sample image. In this project, an RBF neural network using 200 number of centres and using a gaussian spread value of 5 gave the best result in terms of face detection rate, discriminative result, small FAR and FRR as well as the system can detect all faces in a test image commonly used in this research area without indicating a false negative.