Image screening based on projection pursuit for image recognition

A new preprocessing method, called image screening, is presented for improving the recognition rate and efficiency in statistical image recognition. The problem of detecting a specified object in an input image is treated as a two-class classification problem in which the image falls into a set of subimages in the target object (figure) class and the other set of subimages in the ground class. An image screening algorithm selects a candidate set of subimages which are similar to the object class and rejects the remaining set using screening filters whose design is based on projection pursuit. The classifiers of the two classes are taken from the candidate set. Experimental results for eye and mouth detection in face images and text-area detection in document images demonstrate that this method improves the recognition rate and efficiency.