# Automated Visual Attention Detection using Mobile Eye Tracking in Behavioral Classroom Studies

arXiv: https://arxiv.org/abs/2505.07552 · HTML: https://mlsanity.com/paper/2505.07552/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 84 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 21 of 21)

- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [Partial FC: Training 10 Million Identities on a Single Machine](https://mlsanity.com/paper/2010.05222/) (2020)
- [DSFD: Dual Shot Face Detector](https://mlsanity.com/paper/1810.10220/) (2018)
- [Sample and Computation Redistribution for Efficient Face Detection](https://mlsanity.com/paper/2105.04714/) (2021)
- [Killing Two Birds with One Stone:Efficient and Robust Training of Face Recognition CNNs by Partial FC](https://mlsanity.com/paper/2203.15565/) (2022)
- [Event Based, Near Eye Gaze Tracking Beyond 10,000Hz](https://mlsanity.com/paper/2004.03577/) (2020)
- [FaceBoxes: A CPU Real-time Face Detector with High Accuracy](https://mlsanity.com/paper/1708.05234/) (2017)
- [Unsupervised Representation Learning for Gaze Estimation](https://mlsanity.com/paper/1911.06939/) (2019)
- [On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques](https://mlsanity.com/paper/1805.11529/) (2018)
- [TinaFace: Strong but Simple Baseline for Face Detection](https://mlsanity.com/paper/2011.13183/) (2020)
- [Multimodal Engagement Analysis from Facial Videos in the Classroom](https://mlsanity.com/paper/2101.04215/) (2021)
- [A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition](https://mlsanity.com/paper/1606.02894/) (2016)
- [Real-Time Gaze Tracking with Event-Driven Eye Segmentation](https://mlsanity.com/paper/2201.07367/) (2022)
- [Light-weight Head Pose Invariant Gaze Tracking](https://mlsanity.com/paper/1804.08572/) (2018)
- [Differential Privacy for Eye Tracking with Temporal Correlations](https://mlsanity.com/paper/2002.08972/) (2020)
- [Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework](https://mlsanity.com/paper/1911.07936/) (2019)
- [Benefits of temporal information for appearance-based gaze estimation](https://mlsanity.com/paper/2005.11670/) (2020)
- [Automated Anonymisation of Visual and Audio Data in Classroom Studies](https://mlsanity.com/paper/2001.05080/) (2020)
- [Vehicle-counting with Automatic Region-of-Interest and Driving-Trajectory detection](https://mlsanity.com/paper/2108.07135/) (2021)
- [Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking](https://mlsanity.com/paper/2404.06216/) (2024)

## Built on by (most cited first, 2 of 2)

- [Exploring Automated Recognition of Instructional Activity and Discourse from Multimodal Classroom Data](https://mlsanity.com/paper/2512.00087/) (2025)
- [Exploring the Application of Visual Question Answering (VQA) for Classroom Activity Monitoring](https://mlsanity.com/paper/2507.22369/) (2025)

## Abstract (arXiv)

Teachers' visual attention and its distribution across the students in classrooms can constitute important implications for student engagement, achievement, and professional teacher training. Despite that, inferring the information about where and which student teachers focus on is not trivial. Mobile eye tracking can provide vital help to solve this issue; however, the use of mobile eye tracking alone requires a significant amount of manual annotations. To address this limitation, we present an automated processing pipeline concept that requires minimal manually annotated data to recognize which student the teachers focus on. To this end, we utilize state-of-the-art face detection models and face recognition feature embeddings to train face recognition models with transfer learning in the classroom context and combine these models with the teachers' gaze from mobile eye trackers. We evaluated our approach with data collected from four different classrooms, and our results show that while it is possible to estimate the visually focused students with reasonable performance in all of our classroom setups, U-shaped and small classrooms led to the best results with accuracies of approximately 0.7 and 0.9, respectively. While we did not evaluate our method for teacher-student interactions and focused on the validity of the technical approach, as our methodology does not require a vast amount of manually annotated data and offers a non-intrusive way of handling teachers' visual attention, it could help improve instructional strategies, enhance classroom management, and provide feedback for professional teacher development.