Identity extraction from clusters of multi-modal observations

Abstract

In this paper, we present a method for identity extraction from TV News Broadcasts. We define the identity as a set of multi-modal observations. In our case it is the face of a person and a name of a person. The method is based on agglomerative clustering of observations. The resulting clusters represent individual identities, that appeared in the broadcasts. To evaluate the accuracy of our system, we hand labelled approximately one year worth of TV News broadcasts. This resulted in total of 10301 multi-modal observations and 2563 unique identities. Our method achieved a coverage measure of 90.69 % and precision measure of 94.69 %. Given the simplicity of the proposed algorithm, these results are very satisfactory. Furthermore, the designed system is modular and new modalities can be easily added.