Trusting Skype: learning the way people chat for fast user recognition and verification

Roffo, G. , Cristani, M., Bazzani, L., Minh, H. Q. and Murino, V. (2013) Trusting Skype: learning the way people chat for fast user recognition and verification. In: 2013 IEEE International Conference on Computer Vision Workshops (ICCVW), Sydney, Australia, 02-08 Dec 2013, pp. 748-754. ISBN 9781479930227 (doi: 10.1109/ICCVW.2013.102)

Abstract

Identity safekeeping on chats has recently become an important problem on social networks. One of the most important issues is identity theft, where impostors steal the identity of a person, substituting her in the chats, in order to have access to private information. In the literature, the problem has been addressed by designing sets of features which capture the way a person interacts through the chats. However, such approaches perform well only on the long term, after a long conversation has been performed, this is a problem, since in the early turns of a conversation, much important information can be stolen. This paper focuses on this issue, presenting a learning approach which boosts the performance of user recognition and verification, allowing to recognize a subject with considerable accuracy. The proposed method is based on a recent framework of one-shot multi-class multi-view learning, based on Reproducing Kernel Hilbert Spaces (RKHS) theory. Our technique reaches a recognition rate of 76.9% in terms of AUC of the Cumulative Matching Characteristic curve, with only 10 conversational turns considered, on a total of 78 subjects. This sets the new best performances on a public conversation benchmark.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Roffo, Dr Giorgio
• Authors: Roffo, G., Cristani, M., Bazzani, L., Minh, H. Q., and Murino, V.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781479930227