LocalEyenet: Deep Attention framework for Localization of Eyes
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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.
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References · 29
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2017Squeeze-and-Excitation Networkscited by 4,763
- 2018CBAM: Convolutional Block Attention Modulecited by 2,463
- 2017Non-local Neural Networkscited by 2,290
- 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
- 2016Stacked Hourglass Networks for Human Pose Estimationcited by 913
- 2017Deep Layer Aggregationcited by 436
- 2019Adaptive Wing Loss for Robust Face Alignment via Heatmap Regressioncited by 127
- 2017Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networkscited by 108
- 20182D/3D Pose Estimation and Action Recognition using Multitask Deep Learningcited by 103
- 2020LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihoodcited by 87
- 2020Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wildcited by 74
17 more
- 2018Facial Landmark Detection: a Literature Surveycited by 63
- 2017Human Pose Regression by Combining Indirect Part Detection and Contextual Informationcited by 52
- 2020Structured Landmark Detection via Topology-Adapting Deep Graph Learningcited by 50
- 2019DeCaFA: Deep Convolutional Cascade for Face Alignment In The Wildcited by 46
- 2022Towards Accurate Facial Landmark Detection via Cascaded Transformerscited by 31
- 2019Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detectioncited by 28
- 2018Quantized Densely Connected U-Nets for Efficient Landmark Localizationcited by 20
- 2018Gaze-based, Context-aware Robotic System for Assisted Reaching and Graspingcited by 17
- 2018Stacked Dense U-Nets with Dual Transformers for Robust Face Alignmentcited by 17
- 2019Semantic Alignment: Finding Semantically Consistent Ground-truth for Facial Landmark Detectioncited by 16
- 2020Robust Facial Landmark Detection by Multi-order Multi-constraint Deep Networkscited by 14
- 2017Joint Multi-view Face Alignment in the Wildcited by 11
- 2020Gaussian Vector: An Efficient Solution for Facial Landmark Detectioncited by 7
- 2019Spatial-Aware Non-Local Attention for Fashion Landmark Detectioncited by 6
- 2021Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Networkcited by 6
- 2020Robust Facial Landmark Detection by Cross-order Cross-semantic Deep Networkcited by 4
- 2015Deep Recurrent Regression for Facial Landmark Detectioncited by 2
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