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Related Experiment Video

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Published on: August 30, 2013

A mosaicing scheme for pose-invariant face recognition.

Richa Singh1, Mayank Vatsa, Arun Ross

  • 1Lane Department of Computer Science and Engineering, West Virginia University, Morgantown, WV 26506-6109, USA. richas@csee.wvu.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 12, 2007
PubMed
Summary

This study introduces face mosaicing, a method that combines frontal and profile images into a single composite face image. This technique simplifies storage and improves face recognition accuracy by handling pose variations effectively.

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Area of Science:

  • Computer Vision
  • Biometrics
  • Image Processing

Background:

  • Storing multiple face templates for different poses is inefficient.
  • Pose variations in facial images pose a significant challenge for recognition systems.

Purpose of the Study:

  • To develop a face mosaicing scheme for creating a composite face image from frontal and semi-profile views.
  • To eliminate the need for storing multiple face templates per user.
  • To enhance face recognition accuracy by mitigating pose variations.

Main Methods:

  • A hierarchical registration algorithm aligns semi-profile images with a frontal image.
  • Multiresolution splining is employed to blend the registered images into a composite face.
  • A modified C2 algorithm is used for texture-based face recognition against the composite mosaic.

Main Results:

  • The proposed face mosaicing scheme successfully generates a composite face image.
  • The method effectively accounts for pose variations commonly found in facial images.
  • Experiments on three databases demonstrate significant benefits in face recognition performance.

Conclusions:

  • Face mosaicing offers a robust solution for managing pose variations in face recognition.
  • The technique simplifies biometric data storage by consolidating multiple poses into one template.
  • This approach enhances the overall accuracy and efficiency of face recognition systems.