# Onfocus Detection: Identifying Individual-Camera Eye Contact from Unconstrained Images

arXiv: https://arxiv.org/abs/2103.15307 · HTML: https://mlsanity.com/paper/2103.15307/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://mlsanity.com/paper/1910.10088/) (2019)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1611.08860/) (2016)
- [Learning to Navigate for Fine-grained Classification](https://mlsanity.com/paper/1809.00287/) (2018)
- [Convolutional Networks with Dense Connectivity](https://mlsanity.com/paper/2001.02394/) (2020)
- [A Coarse-to-Fine Adaptive Network for Appearance-Based Gaze Estimation](https://mlsanity.com/paper/2001.00187/) (2020)
- [Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization](https://mlsanity.com/paper/1712.01034/) (2017)
- [Between-class Learning for Image Classification](https://mlsanity.com/paper/1711.10284/) (2017)
- [Deep Pictorial Gaze Estimation](https://mlsanity.com/paper/1807.10002/) (2018)
- [Detecting Gaze Towards Eyes in Natural Social Interactions and its Use in Child Assessment](https://mlsanity.com/paper/1902.00607/) (2019)
- [Adaptively Connected Neural Networks](https://mlsanity.com/paper/1904.03579/) (2019)

## Built on by (most cited first, 6 of 6)

- [Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks](https://mlsanity.com/paper/2212.01806/) (2022)
- [Rectify ViT Shortcut Learning by Visual Saliency](https://mlsanity.com/paper/2206.08567/) (2022)
- [Revisiting Long-tailed Image Classification: Survey and Benchmarks with New Evaluation Metrics](https://mlsanity.com/paper/2302.01507/) (2023)
- [GazeTarget360: Towards Gaze Target Estimation in 360-Degree for Robot Perception](https://mlsanity.com/paper/2507.00253/) (2025)
- [Affordance detection with Dynamic-Tree Capsule Networks](https://mlsanity.com/paper/2211.05200/) (2022)
- [NITEC: Versatile Hand-Annotated Eye Contact Dataset for Ego-Vision Interaction](https://mlsanity.com/paper/2311.04505/) (2023)

## Abstract (arXiv)

Onfocus detection aims at identifying whether the focus of the individual captured by a camera is on the camera or not. Based on the behavioral research, the focus of an individual during face-to-camera communication leads to a special type of eye contact, i.e., the individual-camera eye contact, which is a powerful signal in social communication and plays a crucial role in recognizing irregular individual status (e.g., lying or suffering mental disease) and special purposes (e.g., seeking help or attracting fans). Thus, developing effective onfocus detection algorithms is of significance for assisting the criminal investigation, disease discovery, and social behavior analysis. However, the review of the literature shows that very few efforts have been made toward the development of onfocus detector due to the lack of large-scale public available datasets as well as the challenging nature of this task. To this end, this paper engages in the onfocus detection research by addressing the above two issues. Firstly, we build a large-scale onfocus detection dataset, named as the OnFocus Detection In the Wild (OFDIW). It consists of 20,623 images in unconstrained capture conditions (thus called ``in the wild'') and contains individuals with diverse emotions, ages, facial characteristics, and rich interactions with surrounding objects and background scenes. On top of that, we propose a novel end-to-end deep model, i.e., the eye-context interaction inferring network (ECIIN), for onfocus detection, which explores eye-context interaction via dynamic capsule routing. Finally, comprehensive experiments are conducted on the proposed OFDIW dataset to benchmark the existing learning models and demonstrate the effectiveness of the proposed ECIIN. The project (containing both datasets and codes) is at https://github.com/wintercho/focus.