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A Hierarchical Multi-Resolution Self-Supervised Framework for High-Fidelity 3D Face Reconstruction Using Learnable Gabor-Aware Texture Modeling.

Journal of imaging·2026

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A general framework for face reconstruction using single still image based on 2D-to-3D transformation kernel.

Rerkchai Fooprateepsiri¹, Werasak Kurutach¹

¹Faculty of Information Science and Technology, Mahanakorn University and Technology, 140 Cheum-sampan Rd., Nongchok, Bangkok 10530, Thailand.

Forensic Science International

|February 18, 2014

Summary

This study enhances face authentication accuracy by reconstructing 3D face models from single images. Realistic virtual faces with varied poses improve recognition under challenging conditions.

Keywords:

Face detection Face recognition Face reconstruction Gradient vector flow View synthesis

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Area of Science:

Computer Science
Biometrics
Computer Vision

Background:

Face authentication accuracy decreases with variations in pose, illumination, and expression.
Existing feature-based face recognition systems struggle with differing image conditions.

Purpose of the Study:

To improve the accuracy of feature-based face recognition systems.
To develop a method robust to variations in facial pose, illumination, and expression.

Main Methods:

A 2D-to-3D integrated face reconstruction approach creates personalized 3D face models from a single frontal image.
Realistic virtual faces with diverse poses are synthesized from the 3D model to represent the face subspace.
Face recognition is performed using these synthesized virtual face samples.

Main Results:

The proposed framework requires only a single frontal face image for enrollment, simplifying the process.
Synthesized face samples enable robust recognition under challenging conditions such as complex poses, illumination, and expressions.
Experimental results demonstrate improved face recognition accuracy across varying poses, illumination, and expressions.

Conclusions:

The developed method significantly enhances face recognition accuracy.
The approach offers a practical solution for face authentication in real-world scenarios with uncontrolled imaging conditions.