  This paper presented 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 Radial Basis Function (RBF) Neural Network was used to distinguish
between face and non-face. 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 value of center and spread of the RBF. In this paper, fixed spread
value will be used. The Radial Basis Function Neural Network (RBFNN) used to
distinguish faces and non-faces and the evaluation of the system will be the
performance of detection, False Acceptance Rate (FAR), False Rejection Rate
(FRR) and the discriminative properties.
