1“Faved! ” Biometrics: Tell Me Which Image You Like and I’ll Tell You Who You Are

Abstract—This paper builds upon the belief that every human being has a built-in image aesthetic evaluation system. This sort of “personal aesthetics ” mostly follows certain aesthetic rules widely studied in image aesthetics (e.g., rules of thirds, colorfulness, etc.) though it likely contains some innate, unique preferences. This paper is a proof of concept of this intuition, presenting personal aesthetics as a novel behavioral biometrical trait. In our scenario, personal aesthetics activate when an individual is presented with a set of photos he may like or dislike: the goal is to distill and encode the uniqueness of his visual preferences into a compact template. To this aim, we extract a pool of low- and high-level state-of-the-art image features from a set of Flickr images preferred by a user, feeding them successively into a LASSO regressor. LASSO highlights the most discriminant cues for the individual, allowing authentication and recognition tasks. The results are surprising: given only 1 image as test, we can match the user identity against a gallery of 200 individuals definitely much better than chance; using 20 images (all preferred by a single user) as a biometrical trait, we reach an AUC of 96%, considering the Cumulative Matching Characteristic curve. Extensive experiments also support the interpretability of our approach, effectively modelling what is the “what we like ” which distinguishes us from the others. Index Terms—personal aesthetics, image preferences, behav-ioral biometrics, computational aesthetics I