Trusting Skype: Learning the Way People Chat for Fast User Recognition and Verification

Giorgio Roffo, Marco Cristani, Loris Bazzani, Ha Quang Minh, Vittorio Murino; Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2013, pp. 748-754

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Roffo_2013_ICCV_Workshops,
author = {Giorgio Roffo and Marco Cristani and Loris Bazzani and Ha Quang Minh and Vittorio Murino},
title = {Trusting Skype: Learning the Way People Chat for Fast User Recognition and Verification},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {June},
year = {2013}
}