Automated Video Labelling: Identifying Faces by Corroborative Evidence

TL;DR

This paper introduces an automated face labeling system for videos that leverages corroborative evidence from visual and audio sources, enabling scalable and high-precision indexing of large video archives without manual supervision.

Contribution

The paper presents a novel method for determining if a person is famous using image-search engines, facilitating reliable face-identity modeling for automatic labeling.

Findings

Works across multiple video domains without domain adaptation.

Achieves state-of-the-art results on public benchmarks.

Effectively labels faces of both famous and less-famous individuals.

Abstract

We present a method for automatically labelling all faces in video archives, such as TV broadcasts, by combining multiple evidence sources and multiple modalities (visual and audio). We target the problem of ever-growing online video archives, where an effective, scalable indexing solution cannot require a user to provide manual annotation or supervision. To this end, we make three key contributions: (1) We provide a novel, simple, method for determining if a person is famous or not using image-search engines. In turn this enables a face-identity model to be built reliably and robustly, and used for high precision automatic labelling; (2) We show that even for less-famous people, image-search engines can then be used for corroborative evidence to accurately label faces that are named in the scene or the speech; (3) Finally, we quantitatively demonstrate the benefits of our approach on different video domains and test settings, such as TV shows and news broadcasts. Our method works across three disparate datasets without any explicit domain adaptation, and sets new state-of-the-art results on all the public benchmarks.