# Facial Descriptors for Human Interaction Recognition In Still Images

arXiv: https://arxiv.org/abs/1509.05366 · HTML: https://mlsanity.com/paper/1509.05366/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2015.

## Built on by (most cited first, 7 of 7)

- [My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymization](https://mlsanity.com/paper/2211.01361/) (2022)
- [JRDB-Social: A Multifaceted Robotic Dataset for Understanding of Context and Dynamics of Human Interactions Within Social Groups](https://mlsanity.com/paper/2404.04458/) (2024)
- [Detecting Human Artifacts from Text-to-Image Models](https://mlsanity.com/paper/2411.13842/) (2024)
- [CycDA: Unsupervised Cycle Domain Adaptation from Image to Video](https://mlsanity.com/paper/2203.16244/) (2022)
- [My Body My Choice: Human-Centric Full-Body Anonymization](https://mlsanity.com/paper/2406.09553/) (2024)
- [Ask, Pose, Unite: Scaling Data Acquisition for Close Interactions with Vision Language Models](https://mlsanity.com/paper/2410.00309/) (2024)
- [HEIR: Learning Human-Entity Interactions with Functional Roles](https://mlsanity.com/paper/2609.35955/) (2026)

## Abstract (arXiv)

This paper presents a novel approach in a rarely studied area of computer vision: Human interaction recognition in still images. We explore whether the facial regions and their spatial configurations contribute to the recognition of interactions. In this respect, our method involves extraction of several visual features from the facial regions, as well as incorporation of scene characteristics and deep features to the recognition. Extracted multiple features are utilized within a discriminative learning framework for recognizing interactions between people. Our designed facial descriptors are based on the observation that relative positions, size and locations of the faces are likely to be important for characterizing human interactions. Since there is no available dataset in this relatively new domain, a comprehensive new dataset which includes several images of human interactions is collected. Our experimental results show that faces and scene characteristics contain important information to recognize interactions between people.