The Effect of Overlapping Spread Value for Radial Basis Function Neural Network in Face Detection

In this paper, the effect of overlapping spread value for Radial Basis Function Neural Network (RBFNN) in face detection is presented. The reason for taking the overlapping factor into consideration is to optimize the results for using variance 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. RBFNN 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. The performance of the RBFNN face detection system will be based on the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) criteria.