# Automatic Main Character Recognition for Photographic Studies

arXiv: https://arxiv.org/abs/2106.09064 · HTML: https://mlsanity.com/paper/2106.09064/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [Hand Keypoint Detection in Single Images using Multiview Bootstrapping](https://mlsanity.com/paper/1704.07809/) (2017)
- [Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition](https://mlsanity.com/paper/1811.00270/) (2018)
- [Max-Margin Object Detection](https://mlsanity.com/paper/1502.00046/) (2015)
- [When CNNs Meet Random RNNs: Towards Multi-Level Analysis for RGB-D Object and Scene Recognition](https://mlsanity.com/paper/2004.12349/) (2020)
- [Learning to Learn Relation for Important People Detection in Still Images](https://mlsanity.com/paper/1904.03632/) (2019)
- [Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection](https://mlsanity.com/paper/2004.07568/) (2020)
- [Single Image Human Proxemics Estimation for Visual Social Distancing](https://mlsanity.com/paper/2011.02018/) (2020)
- [PersonRank: Detecting Important People in Images](https://mlsanity.com/paper/1711.01984/) (2017)
- [Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm](https://mlsanity.com/paper/2103.06759/) (2021)
- [VIP: Finding Important People in Images](https://mlsanity.com/paper/1502.05678/) (2015)

## Built on by (most cited first, 1 of 1)

- [Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives](https://mlsanity.com/paper/2204.02149/) (2022)

## Abstract (arXiv)

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 can be 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.