Abstract: In this paper, a new approach of face detection system is developed. This system develops the algorithm for computing the accurate measurement of face features. The task of detecting and locating human faces in arbitrary images is complex due to the variability present across human faces, including skin color, pose, expression, position and orientation, and the presence of ‘facial furniture’ such as glasses or facial hair. In this system, a neural network-based upright frontal face detection system is presented. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. A straightforward procedure for aligning positive face examples for training was presented. It uses a transformer that converts an image of human face into a feature vector, which will then be compared with the feature vectors of a training set of human faces to classify the image. Some mathematical concepts are used to calculate the distance and angles between feature points. And histogram equalization is used to enhance the selected feature. In this paper, faces are chosen because it can generate the significant features for human face than other techniques. Finally, matching is accomplished by detecting the test photo. For programming and simulation of this system, MATLAB software is applied. The neural network toolbox “nntool” is called from the main function for training system. This research develops a simple face detection system for to provide the security system.