This paper introduces the new idea of describing people using first names. We show that describing people in terms of similarity to a vector of possible first names is a powerful representation of facial appearance that can be used for a number of important applications, such as naming never-seen faces and building facial attribute classifiers. We build models for 100 common first names used in the United States and for each pair, construct a pairwise first-name classifier. These classifiers are built using training images downloaded from the internet, with no additional user interaction. This gives our approach important advantages in building practical systems that do not require additional human intervention for data labeling. The classification scores from each pairwise name classifier can be used as a set of facial attributes to describe facial appearance. We show several surprising results. Our name attributes predict the correct first names of test faces at rates far greater than chance. The name attributes are applied to gender recognition and to age classification, outperforming state-of-the-art methods with all training images automatically gathered from the internet. We also demonstrate the powerful use of our name attributes for associating faces in images with names from caption, and the important application of unconstrained face verification.
multi-feature fusion, Facial processing, attributes learning, social contexts
Andrew Gallagher, "The Hidden Sides of Names - Face Modeling With First Name Attributes", IEEE Transactions on Pattern Analysis & Machine Intelligence, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TPAMI.2014.2302443