# DAiSEE: Towards User Engagement Recognition in the Wild

arXiv: https://arxiv.org/abs/1609.01885 · HTML: https://mlsanity.com/paper/1609.01885/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2016.

## Built on by (most cited first, 30 of 39)

- [Improving state-of-the-art in Detecting Student Engagement with Resnet and TCN Hybrid Network](https://mlsanity.com/paper/2104.10122/) (2021)
- [Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines](https://mlsanity.com/paper/2302.00431/) (2023)
- [Affect-driven Ordinal Engagement Measurement from Video](https://mlsanity.com/paper/2106.10882/) (2021)
- [AffectNet+: A Database for Enhancing Facial Expression Recognition with Soft-Labels](https://mlsanity.com/paper/2410.22506/) (2024)
- [Engagement Detection with Multi-Task Training in E-Learning Environments](https://mlsanity.com/paper/2204.04020/) (2022)
- [DMCNet: Diversified Model Combination Network for Understanding Engagement from Video Screengrabs](https://mlsanity.com/paper/2204.06454/) (2022)
- [Class-attention Video Transformer for Engagement Intensity Prediction](https://mlsanity.com/paper/2208.07216/) (2022)
- [Engagement Measurement Based on Facial Landmarks and Spatial-Temporal Graph Convolutional Networks](https://mlsanity.com/paper/2403.17175/) (2024)
- [TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals](https://mlsanity.com/paper/2404.09474/) (2024)
- [Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder](https://mlsanity.com/paper/2211.06870/) (2022)
- [Bag of States: A Non-sequential Approach to Video-based Engagement Measurement](https://mlsanity.com/paper/2301.06730/) (2023)
- [Analyzing Participants' Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features](https://mlsanity.com/paper/2404.04394/) (2024)
- [Explainable graph attention network for stress recognition (StressGAT) via differential action units](https://mlsanity.com/paper/2607.20819/) (2026)
- [Automatic Context-Driven Inference of Engagement in HMI: A Survey](https://mlsanity.com/paper/2209.15370/) (2022)
- [Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey](https://mlsanity.com/paper/2403.08824/) (2024)
- [A General Model for Detecting Learner Engagement: Implementation and Evaluation](https://mlsanity.com/paper/2405.04251/) (2024)
- [VLM-SubtleBench: How Far Are VLMs from Human-Level Subtle Comparative Reasoning?](https://mlsanity.com/paper/2603.07888/) (2026)
- [CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels](https://mlsanity.com/paper/2312.09066/) (2023)
- [ViBED-Net: Video Based Engagement Detection Network Using Face-Aware and Scene-Aware Spatiotemporal Cues](https://mlsanity.com/paper/2510.18016/) (2025)
- [Supervised Contrastive Learning for Ordinal Engagement Measurement](https://mlsanity.com/paper/2505.20676/) (2025)
- [GazeTarget360: Towards Gaze Target Estimation in 360-Degree for Robot Perception](https://mlsanity.com/paper/2507.00253/) (2025)
- [PriorNet: Prior-Guided Engagement Estimation from Face Video](https://mlsanity.com/paper/2605.03615/) (2026)
- [Inconsistencies in the Definition and Annotation of Student Engagement in Virtual Learning Datasets: A Critical Review](https://mlsanity.com/paper/2208.04548/) (2022)
- [Real-time estimation of overt attention from dynamic features of the face using deep-learning](https://mlsanity.com/paper/2409.13084/) (2024)
- [VisioPhysioENet: Visual Physiological Engagement Detection Network](https://mlsanity.com/paper/2409.16126/) (2024)
- [Learner Attentiveness and Engagement Analysis in Online Education Using Computer Vision](https://mlsanity.com/paper/2412.00429/) (2024)
- [Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy](https://mlsanity.com/paper/2502.07404/) (2025)
- [DIPSER: A Dataset for In-Person Student Engagement Recognition in the Wild](https://mlsanity.com/paper/2502.20209/) (2025)
- [OPEN: A Benchmark Dataset and Baseline for Older Adult Patient Engagement Recognition in Virtual Rehabilitation Learning Environments](https://mlsanity.com/paper/2507.17959/) (2025)
- [Safeguarding Privacy: Privacy-Preserving Detection of Mind Wandering and Disengagement Using Federated Learning in Online Education](https://mlsanity.com/paper/2602.09904/) (2026)

## Abstract (arXiv)

We introduce DAiSEE, the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration in the wild. The dataset has four levels of labels namely - very low, low, high, and very high for each of the affective states, which are crowd annotated and correlated with a gold standard annotation created using a team of expert psychologists. We have also established benchmark results on this dataset using state-of-the-art video classification methods that are available today. We believe that DAiSEE will provide the research community with challenges in feature extraction, context-based inference, and development of suitable machine learning methods for related tasks, thus providing a springboard for further research. The dataset is available for download at https://people.iith.ac.in/vineethnb/resources/daisee/index.html.