The goal of elastic bunch graph matching on a probe image is to find the fiducial points and thus to extract from the image a graph which maximizes the similarity with the FBG as defined in Equation (5). Here the Elastic Bunch Graph Matching is proposed for surgically altered face image from which we expecting good result. The Elastic Bunch Graph Matching abbreviated as EBGM is already applicable on normal face image which provides best results as much as possible when compared with database of that image. EBGM is an algorithm in image.
. Part of the book series (LNCS, volume 1296) Abstract We present a system for recognizing human faces from single images out of a large database with one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial points on the face (eyes, mouth etc.) are described by sets of wavelet components ( jets).
Image graph extraction is based on a novel approach, the bunch graph, which is constructed from a small set of sample image graphs. Recognition is based on a straight-forward comparison of image graphs. We report recognition experiments on the FERET database and the Bochum database, including recognition across pose.
. Part of the book series (LNCS, volume 1296) Abstract We present a system for recognizing human faces from single images out of a large database with one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial points on the face (eyes, mouth etc.) are described by sets of wavelet components ( jets).
Image graph extraction is based on a novel approach, the bunch graph, which is constructed from a small set of sample image graphs. Recognition is based on a straight-forward comparison of image graphs. We report recognition experiments on the FERET database and the Bochum database, including recognition across pose.