This paper presents a neural network-based upright frontal face detection system. Generally object detection is the problem of determining whether or not a sub-window of an image belongs to the set of images of an object of interest. The system task is to detect and locate upright frontal human faces, which exist in a grayscale image that contains human faces against cluttered background. The system introduces some solutions to the problems related to the face detection domain. It arbitrates between multiple neural networks and heuristics, such as the fact that faces rarely overlap in images, to improve the performance and accuracy of the used algorithm. We used a new preliminary algorithm to increase the speed of the system to 3 to 10 times faster than other systems |
