Face Detection, Tracking, and Classification from Large-Scale News Archives for Analysis of Key Political Figures

IF 4.7 2区社会学 Q1 POLITICAL SCIENCE

Political Analysis Pub Date : 2023-11-06 DOI:10.1017/pan.2023.33

Andreu Girbau, Tetsuro Kobayashi, Benjamin Renoust, Yusuke Matsui, Shin’ichi Satoh

引用次数: 0

Abstract

Abstract Analyzing the appearances of political figures in large-scale news archives is increasingly important with the growing availability of large-scale news archives and developments in computer vision. We present a deep learning-based method combining face detection, tracking, and classification, which is particularly unique because it does not require any re-training when targeting new individuals. Users can feed only a few images of target individuals to reliably detect, track, and classify them. Extensive validation of prominent political figures in two news archives spanning 10 to 20 years, one containing three U.S. cable news and the other including two major Japanese news programs, consistently shows high performance and flexibility of the proposed method. The codes are made readily available to the public.

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大型新闻档案中的人脸检测、追踪与分类，用于关键政治人物分析

随着大规模新闻档案的日益普及和计算机视觉技术的发展，分析政治人物在大型新闻档案中的形象显得越来越重要。我们提出了一种结合人脸检测、跟踪和分类的基于深度学习的方法，这种方法特别独特，因为它在针对新个体时不需要任何重新训练。用户可以只提供目标个体的少量图像来可靠地检测、跟踪和分类他们。通过对两个新闻档案中杰出政治人物的广泛验证，其中一个包含三个美国有线电视新闻，另一个包括两个主要的日本新闻节目，一致显示了所提出方法的高性能和灵活性。这些守则可供公众随时索取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Political Analysis POLITICAL SCIENCE-

CiteScore

8.80

自引率

3.70%

发文量

期刊介绍： Political Analysis chronicles these exciting developments by publishing the most sophisticated scholarship in the field. It is the place to learn new methods, to find some of the best empirical scholarship, and to publish your best research.