Improving Automatic Name-Face Association using Celebrity Images on the Web

This paper investigates the task of automatically associating faces appearing in images (or videos) with their names. Our novelty lies in the use of celebrity Web images to facilitate the task. Specifically, we first propose a method named Image Matching (IM), which uses the faces in images returned from name queries over an image search engine as the gallery set of the names, and a probe face is classified as one of the names, or none of them, according to their matching scores and compatibility characterized by a proposed Assigning-Thresholding (AT) pipeline. Noting IM could provide guidance for association for the well-established Graph-based Association (GA), we further propose two methods that jointly utilize the two kinds of complementary cues. They are: the early fusion of IM and GA (EF-IMGA) that takes the IM score as an additional information source to help the association in GA, and the late fusion of IM and GA (LF-IMGA) that combines the scores from both IM and GA obtained individually to make the association. Evaluations on datasets of captioned news images and Web videos both show the proposed methods, especially the two fused ones, provide significant improvements over GA.