Automatic Main Character Recognition for Photographic Studies

Mert Seker, Anssi Männistö, Alexandros Iosifidis, Jenni Raitoharju

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Main characters in images are the most important humans that catch the viewer's attention upon first look, and they are emphasized by properties such as size, position, color saturation, and sharpness of focus. Identifying the main character in images plays an important role in traditional photographic studies and media analysis, but the task is performed manually and is, thus, slow and laborious. Furthermore, selection of main characters can be sometimes subjective. In this paper, we analyze the feasibility of solving the main character recognition needed for photographic studies automatically and propose a method for identifying the main characters. The proposed method uses machine learning based human pose estimation along with traditional computer vision approaches for this task. We approach the task as a binary classification problem where each detected human is classified either as a main character or not. To evaluate both the subjectivity of the task and the performance of our method, we collected a dataset of 300 varying images from multiple sources and asked five people, a photographic researcher and four other persons, to annotate the main characters. Our analysis showed a relatively high agreement between different annotators. The proposed method achieved a promising F1 score of 0.83 on the full image set and 0.96 on a subset evaluated as most clear and important cases by the photographic researcher.

Original languageEnglish
Title of host publicationIEEE 23rd International Workshop on Multimedia Signal Processing, MMSP 2021
Number of pages6
ISBN (Electronic)9781665432870
Publication statusPublished - 2021
Publication typeA4 Article in conference proceedings
EventIEEE International Workshop on Multimedia Signal Processing - Tampere, Finland
Duration: 6 Oct 20218 Oct 2021

Publication series

NameIEEE International Workshop on Multimedia Signal Processing
ISSN (Electronic)2473-3628


ConferenceIEEE International Workshop on Multimedia Signal Processing
Abbreviated titleIEEE MMSP 2021
Internet address


  • computer vision
  • human pose estimation
  • machine learning
  • main character recognition
  • photographic studies

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Signal Processing
  • Safety, Risk, Reliability and Quality
  • Media Technology


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