Personal Aesthetics for Soft Biometrics: A Generative Multi-resolution Approach

Abstract

Are we recognizable by our image preferences? This paper answers affirmatively the question, presenting a soft biometric approach where the preferred images of an individual are used as his personal signature in identification tasks. The approach builds a multi-resolution latent space, formed by multiple Counting Grids, where similar images are mapped nearby. On this space, a set of preferred images of a user produces an ensemble of intensity maps, highlighting in an intuitive way his personal aesthetic preferences. These maps are then used for learning a battery of discriminative classifiers (one for each resolution), which characterizes the user and serves to perform identification. Results are promising: on a dataset of 200 users, and 40K images, using 20 preferred images as biometric template gives 66% of probability of guessing the correct user. This makes the "personal aesthetics" a very hot topic for soft biometrics, while its usage in standard biometric applications seems to be far from being effective, as we show in a simple user study.