Reading between the turns: Statistical modeling for identity recognition and verification

Identity safekeeping has recently become an important problem for the social web: as a case study, we focus here on instant messaging platforms, proposing novel soft-biometric cues for user recognition and verification. Specif-ically, we design a set of features encoding effectively how a person converses: since chats are crossbreeds of written text and face-to-face verbal communication, the features in-herit equally from textual authorship attribution and con-versational analysis of speech. Importantly, our cues ig-nore completely the semantics of the chat, relying solely on non-verbal aspects, taking care of possible privacy and ethical issues. We apply our approach on a novel dataset of 94 different individuals, whose chat conversations have been recorded for an average period of five months; recog-nition rate, intended as normalized AUC on CMC curve, is 95.73%, while verification rate amounts to 95.66%, as nor-malized AUC on ROC curve. 1