Biometrics on visual preferences: A “pump and distill” regression approach

We present a statistical behavioural biometric approach for recognizing people by their aesthetic preferences, using colour images. In the enrollment phase, a model is learnt for each user, using a training set of preferred images. In the recognition/authentication phase, such model is tested with an unseen set of pictures preferred by a probe subject. The approach is dubbed “pump and distill”, since the training set of each user is pumped by bagging, producing a set of image ensembles. In the distill step, each ensemble is reduced into a set of surrogates, that is, aggregates of images sharing a similar visual content. Finally, LASSO regression is performed on these surrogates; the resulting regressor, employed as a classifier, takes test images belonging to a single user, predicting his identity. The approach improves the state-of-the-art on recognition and authentication tasks in average, on a dataset of 40000 Flickr images and 200 users. In practice, given a pool of 20 preferred images of a user, the approach recognizes his identity with an accuracy of 92%, and sets an authentication accuracy of 91% in terms of normalized Area Under the Curve of the CMC and ROC curve, respectively.