Face Detection Using Radial Basis Function Neural Networks with Fixed Spread Value

Abstract

This paper present a face detection system using Radial Basis Function Neural Networks With Fixed Spread Value. Face detection is the first step in 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. General preprocessing approach was used for normalizing the image and a Radial Basis Function (RBF) Neural Network was used to distinguish between face and non-face images. RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and spread of the RBF. In this paper, a uniform fixed spread value will be used. The performance of the RBFNN face detection system will be based on the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) criteria. In this research, the best setting for RBF face detection were summarized into one table where by using center 200 and spread 4 gives the highest detection rate and the lowest FAR as well as FRR. But for detecting many faces in a single image, center 200 and spread 5 is the best setting as the system can detect all faces in the image