Original approach for the localization of objects in images

The detection and localization of faces in an image has many applications in various domains: surveillance, TV audience polling, etc. We propose a new method for this task. The main idea of our method is to train a neural network to detect the presence or absence of a face in its input window, and to scan this network over at all possible locations in the image. Because of the nature of the neural network architecture we used, this process can be done very efficiently without requiring to actually recompute the entire network state at each location. The scanning is performed on several versions of the image at various scales, resulting in an efficient, scale independent detector and locator.