Doppelgänger List Comparison

Face recognition approaches have traditionally focused on direct comparisons between aligned images, e.g using pixel values or local image features. Such comparisons become prohibitively difficult when comparing faces across extreme differences in pose, illumination and expression. We propose a novel data driven method based on the insight that comparing images of faces is most meaningful when they are in comparable imaging conditions. To this end we describe an image of a face by an ordered list of identities from a Library. The order of the list is determined by the similarity of the Library images to the probe image. The lists act as a signature for each face image: similarity between face images is determined via the similarity of the signatures.