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Related Experiment Video

Updated: May 9, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

3D face recognition under expressions, occlusions, and pose variations.

Hassen Drira¹, Boulbaba Ben Amor, Anuj Srivastava

¹Laboratoire d'Informatique Fondamentale de Lille-LIFL, UMR CNRS 8022, Institut Mines-Télécom, Villeneuve d'Ascq, France. hassen.drira@telecom-lille1.eu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|July 23, 2013

Summary

This summary is machine-generated.

We developed a new geometric method using elastic shape analysis for 3D face shape comparison. This approach accurately analyzes facial deformations, improving state-of-the-art results on challenging datasets.

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Area of Science:

Computer Vision
Geometric Deep Learning
3D Shape Analysis

Background:

Analyzing 3D face shapes is crucial for applications like facial recognition and medical imaging.
Existing methods struggle with variations in expressions, pose, and occlusions.

Purpose of the Study:

To introduce a novel geometric framework for robust 3D face shape analysis.
To enable accurate comparison, matching, and averaging of 3D facial surfaces.

Main Methods:

Representing facial surfaces using radial curves from the nose tip.
Applying elastic shape analysis within a Riemannian framework.
Utilizing an elastic Riemannian metric for deformation measurement.

Main Results:

Achieved state-of-the-art or improved performance on FRGCv2, GavabDB, and Bosphorus databases.
Demonstrated robustness to large facial expressions, pose variations, and occlusions.
Framework shows promise empirically and theoretically.

Conclusions:

The proposed Riemannian framework offers a natural and robust method for 3D face shape analysis.
Enables formal statistical inferences, including missing part estimation and average shape computation.