# LocalEyenet: Deep Attention framework for Localization of Eyes

arXiv: https://arxiv.org/abs/2303.12728 · HTML: https://mlsanity.com/paper/2303.12728/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 70 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 29 of 29)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression](https://mlsanity.com/paper/1904.07399/) (2019)
- [Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks](https://mlsanity.com/paper/1711.06753/) (2017)
- [2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning](https://mlsanity.com/paper/1802.09232/) (2018)
- [LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood](https://mlsanity.com/paper/2004.02980/) (2020)
- [Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild](https://mlsanity.com/paper/2003.03771/) (2020)
- [Facial Landmark Detection: a Literature Survey](https://mlsanity.com/paper/1805.05563/) (2018)
- [Human Pose Regression by Combining Indirect Part Detection and Contextual Information](https://mlsanity.com/paper/1710.02322/) (2017)
- [Structured Landmark Detection via Topology-Adapting Deep Graph Learning](https://mlsanity.com/paper/2004.08190/) (2020)
- [DeCaFA: Deep Convolutional Cascade for Face Alignment In The Wild](https://mlsanity.com/paper/1904.02549/) (2019)
- [Towards Accurate Facial Landmark Detection via Cascaded Transformers](https://mlsanity.com/paper/2208.10808/) (2022)
- [Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection](https://mlsanity.com/paper/1908.02116/) (2019)
- [Quantized Densely Connected U-Nets for Efficient Landmark Localization](https://mlsanity.com/paper/1808.02194/) (2018)
- [Gaze-based, Context-aware Robotic System for Assisted Reaching and Grasping](https://mlsanity.com/paper/1809.08095/) (2018)
- [Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment](https://mlsanity.com/paper/1812.01936/) (2018)
- [Semantic Alignment: Finding Semantically Consistent Ground-truth for Facial Landmark Detection](https://mlsanity.com/paper/1903.10661/) (2019)
- [Robust Facial Landmark Detection by Multi-order Multi-constraint Deep Networks](https://mlsanity.com/paper/2012.04927/) (2020)
- [Joint Multi-view Face Alignment in the Wild](https://mlsanity.com/paper/1708.06023/) (2017)
- [Gaussian Vector: An Efficient Solution for Facial Landmark Detection](https://mlsanity.com/paper/2010.01318/) (2020)
- [Spatial-Aware Non-Local Attention for Fashion Landmark Detection](https://mlsanity.com/paper/1903.04104/) (2019)
- [Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network](https://mlsanity.com/paper/2112.12328/) (2021)
- [Robust Facial Landmark Detection by Cross-order Cross-semantic Deep Network](https://mlsanity.com/paper/2011.07777/) (2020)
- [Deep Recurrent Regression for Facial Landmark Detection](https://mlsanity.com/paper/1510.09083/) (2015)

## Abstract (arXiv)

Development of human machine interface has become a necessity for modern day machines to catalyze more autonomy and more efficiency. Gaze driven human intervention is an effective and convenient option for creating an interface to alleviate human errors. Facial landmark detection is very crucial for designing a robust gaze detection system. Regression based methods capacitate good spatial localization of the landmarks corresponding to different parts of the faces. But there are still scope of improvements which have been addressed by incorporating attention. In this paper, we have proposed a deep coarse-to-fine architecture called LocalEyenet for localization of only the eye regions that can be trained end-to-end. The model architecture, build on stacked hourglass backbone, learns the self-attention in feature maps which aids in preserving global as well as local spatial dependencies in face image. We have incorporated deep layer aggregation in each hourglass to minimize the loss of attention over the depth of architecture. Our model shows good generalization ability in cross-dataset evaluation and in real-time localization of eyes.