Recognizing People by Their Personal Aesthetics: A Statistical Multi-level Approach

This paper presents a study on personal aesthetics, a recent soft biometrics application where the goal is to recognize people by considering the images they like. Here we propose a multi-level approach, where each level is intended as a low-dimensional space where the images preferred by a user can be projected, and similar images are mapped nearby, namely a Counting Grid. Multiple levels are generated by adopting Counting Grids at different resolutions, corresponding to analyze images at different grains. Each level is then associated to an exemplar Support Vector Machine, which separates the images of an individual from the rest of the users. Putting together multiple levels gives a battery of classifiers whose performances are very good: on a dataset of 200 users, and 40K images, using 5 preferred images as biometric template gives 97% of probability of guessing the correct user; as for the verification capability, the equal error rate is 0.11. The approach has also been tested with diverse comparative methods and different features, showing that color image properties are crucial to encode the personal aesthetics, and that high-level information (as the objects within the images) could be very effective, but current methods are not robust enough to catch it.