BIOMETRICS ON VISUAL PREFERENCES: A “PUMP AND DISTILL” REGRESSION APPROACH

We present a statistical behavioural biometric approach forrecognizing people by their aesthetic preferences, usingcolour images. In the enrollment phase, a model is learntfor each user, using a training set of preferred images. In therecognition/authentication phase, such model is tested withan unseen set of pictures preferred by a probe subject. Theapproach is dubbed “pump and distill”, since the training setof each user is pumped by bagging, producing a set of imageensembles. In the distill step, each ensemble is reduced intoa set of surrogates, that is, aggregates of images sharing asimilar visual content. Finally, LASSO regression is performedon these surrogates; the resulting regressor, employedas a classifier, takes test images belonging to a single user,predicting his identity. The approach improves the state-ofthe-arton recognition and authentication tasks in average, ona dataset of 40000 Flickr images and 200 users. In practice,given a pool of 20 preferred images of a user, the approachrecognizes his identity with an accuracy of 92%, and sets anauthentication accuracy of 91% in terms of normalized AreaUnder the Curve of the CMC and ROC curve, respectively