Finding Person Relations in Image Data of the Internet Archive

TL;DR

This paper presents a deep learning-based system for recognizing persons in web news images from the Internet Archive, enhancing entity tracking by complementing text analysis with image-based person identification.

Contribution

The paper introduces a novel face recognition system tailored for large-scale web news images, enabling more precise tracking of persons in multimedia web archives.

Findings

High accuracy on standard face recognition benchmarks

Successful identification of persons in real-world web news images

Demonstrated system's utility with practical use cases

Abstract

The multimedia content in the World Wide Web is rapidly growing and contains valuable information for many applications in different domains. For this reason, the Internet Archive initiative has been gathering billions of time-versioned web pages since the mid-nineties. However, the huge amount of data is rarely labeled with appropriate metadata and automatic approaches are required to enable semantic search. Normally, the textual content of the Internet Archive is used to extract entities and their possible relations across domains such as politics and entertainment, whereas image and video content is usually neglected. In this paper, we introduce a system for person recognition in image content of web news stored in the Internet Archive. Thus, the system complements entity recognition in text and allows researchers and analysts to track media coverage and relations of persons more precisely. Based on a deep learning face recognition approach, we suggest a system that automatically detects persons of interest and gathers sample material, which is subsequently used to identify them in the image data of the Internet Archive. We evaluate the performance of the face recognition system on an appropriate standard benchmark dataset and demonstrate the feasibility of the approach with two use cases.