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Mathematical Camera Array Optimization for Face 3D Modeling Application.

Bashar Alsadik1, Luuk Spreeuwers2, Farzaneh Dadrass Javan1

  • 1Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7522 NB Enschede, The Netherlands.

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|December 23, 2023
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Summary
This summary is machine-generated.

Designing optimal camera networks is crucial for accurate 3D face modeling. A 7-camera array configuration offers high precision, balancing accuracy and system complexity for reliable facial recognition applications.

Keywords:
3D modelcamera networkconstrained minimizationface recognitionoptimizationphotogrammetry

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Area of Science:

  • Computer Vision
  • Photogrammetry
  • Robotics

Background:

  • Camera network design is complex, impacting applications like 3D face modeling.
  • Achieving high accuracy in 3D face models is challenging due to hardware limitations and suboptimal system configurations.

Purpose of the Study:

  • To present an optimal geometric design methodology for camera networks.
  • To investigate multi-camera system configurations for improved 3D face modeling and recognition.

Main Methods:

  • Applied a mathematical nonlinear constrained optimization technique.
  • Tested configurations of four to eight cameras for facial 3D model quality assessment.

Main Results:

  • A 7-camera array (pentagon with two internal cameras) provides high accuracy.
  • A 9-camera array offers high point density.
  • A 5-camera array presents a balance between accuracy and camera count.

Conclusions:

  • The 7-camera array is optimal for high-accuracy 3D face modeling.
  • Configuration choice depends on prioritizing accuracy, point density, or a balance between the two.