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Prosopagnosia

Prosopagnosia

Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...

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Related Experiment Video

Updated: Jun 10, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Advanced 3D Face Reconstruction from Single 2D Images Using Enhanced Adversarial Neural Networks and Graph Neural

Mohamed Fathallah¹, Sherif Eletriby², Maazen Alsabaan³

¹Department of Computer Science, Faculty of Computers and Information, Kafr El-Sheikh University, Kafr El-Sheikh 33511, Egypt.

Sensors (Basel, Switzerland)

|October 16, 2024

Summary

This summary is machine-generated.

This study introduces a new framework for 3D face reconstruction from 2D images using graph neural networks. The novel approach enhances accuracy and robustness, significantly improving performance over existing methods.

Keywords:

3D reconstruction GANs GCNs efficient-net

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Last Updated: Jun 10, 2025

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Area of Science:

Computer Vision
Machine Learning
3D Graphics

Background:

Existing 3D face reconstruction methods struggle with accuracy and robustness.
Single 2D image-based reconstruction presents significant challenges due to inherent depth ambiguity.

Purpose of the Study:

To develop a novel framework for accurate and robust 3D face reconstruction from single 2D images.
To address limitations in existing methods, particularly mode collapse and instability in generative models.

Main Methods:

Integration of modified adversarial neural networks with graph neural networks (GCNs).
Utilized a GCN-based generator with novel loss functions and identity blocks.
Incorporated facial landmarks and an efficient-net decoder.
Employed a lightweight GCN-based discriminator.

Main Results:

Achieved state-of-the-art performance on 300W-LP and AFLW2000-3D datasets.
Reduced Chamfer Distance by 62.7% and Earth Mover's Distance by 57.1% on 300W-LP.
Demonstrated superior robustness to variations in pose, occlusion, noise, and lighting.

Conclusions:

The proposed framework offers significant improvements in 3D face reconstruction accuracy and stability.
The novel integration of GCNs and adversarial networks provides a robust solution for real-world scenarios.
The method achieves faster processing times compared to existing techniques.