# Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics

arXiv: https://arxiv.org/abs/2201.02110 · HTML: https://mlsanity.com/paper/2201.02110/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 72 references that are in the map.
- First posted on arXiv 5 Jan 2022.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://mlsanity.com/paper/1608.08242/) (2016)
- [PyTorch Metric Learning](https://mlsanity.com/paper/2008.09164/) (2020)
- [Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye](https://mlsanity.com/paper/1906.11889/) (2019)
- [GazeBase: A Large-Scale, Multi-Stimulus, Longitudinal Eye Movement Dataset](https://mlsanity.com/paper/2009.06171/) (2020)
- [A privacy-preserving approach to streaming eye-tracking data](https://mlsanity.com/paper/2102.01770/) (2021)
- [ResNet or DenseNet? Introducing Dense Shortcuts to ResNet](https://mlsanity.com/paper/2010.12496/) (2020)
- [Privacy-Aware Eye Tracking Using Differential Privacy](https://mlsanity.com/paper/1812.08000/) (2018)
- [Eye Know You: Metric Learning for End-to-end Biometric Authentication Using Eye Movements from a Longitudinal Dataset](https://mlsanity.com/paper/2104.10489/) (2021)
- [A Score-level Fusion Method for Eye Movement Biometrics](https://mlsanity.com/paper/1601.03333/) (2016)
- [Differential Privacy for Eye-Tracking Data](https://mlsanity.com/paper/1904.06809/) (2019)
- [Biometric Performance as a Function of Gallery Size](https://mlsanity.com/paper/1906.06272/) (2019)
- [Deep Distributional Sequence Embeddings Based on a Wasserstein Loss](https://mlsanity.com/paper/1912.01933/) (2019)

## Built on by (most cited first, 3 of 3)

- [EmMixformer: Mix transformer for eye movement recognition](https://mlsanity.com/paper/2401.04956/) (2024)
- [EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition](https://mlsanity.com/paper/2409.14432/) (2024)
- [Deep Learning-Based Visual Fatigue Detection Using Eye Gaze Patterns in VR](https://mlsanity.com/paper/2510.12994/) (2025)

## Abstract (arXiv)

Eye movement biometrics (EMB) is a relatively recent behavioral biometric modality that may have the potential to become the primary authentication method in virtual- and augmented-reality devices due to their emerging use of eye-tracking sensors to enable foveated rendering techniques. However, existing EMB models have yet to demonstrate levels of performance that would be acceptable for real-world use. Deep learning approaches to EMB have largely employed plain convolutional neural networks (CNNs), but there have been many milestone improvements to convolutional architectures over the years including residual networks (ResNets) and densely connected convolutional networks (DenseNets). The present study employs a DenseNet architecture for end-to-end EMB and compares the proposed model against the most relevant prior works. The proposed technique not only outperforms the previous state of the art, but is also the first to approach a level of authentication performance that would be acceptable for real-world use.