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Fast face localization and verification J.Matas, K.Johnson,J.Kittler. Presented by: Dong Xie. Introduction. Personal identification (authentication, verification of identity) – security applications. Identification vs. Recognition Small number of reference images vs. larger database

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fast face localization and verification j matas k johnson j kittler

Fast face localization and verificationJ.Matas, K.Johnson,J.Kittler

Presented by: Dong Xie

introduction
Introduction
  • Personal identification (authentication, verification of identity) – security applications.
  • Identification vs. Recognition
    • Small number of reference images vs. larger database
    • Near real-time vs. w/o time constraint
    • Previously unseen person vs. image from training database
in this article
In this article…
  • They propose an identification method based on optimized robust correlation.
    • An integrated approach: localization, normalization as well as identification is achieved simultaneously.
    • To that end, a robust form of correlation is evaluated inside an optimization loop.
    • Random sampling to speed up evaluation of the cost function inside the optimization loop.
optimized robust correlation
Optimized robust correlation…
  • Objective: find the global extremum in a multi-dimensional search space that corresponds to the best match between a pair of images
  • Score function:

A combined score function.

  • Optimization method:
    • Each iteration, the transformation between reference and test image is perturbed by adding a random vector drawn from an exponential distribution
    • New transformation is accepted only if score was increased.
  • Random sampling
slide5

M2VTS Multi-modal Database:

5 ‘shots’/person over a period of several weeks

slide6

Example of output

3a-d Successful

Se-h Failed

slide8

Performance of the optimized robust correlation

  • Equal Error Rate(EER): (a)search method.(b)number of test images used
  • Near Real time (0.24s/single identification):
    • (c) search method(client test) (d) client and imposter.
slide9

EER for Optimized Robust Correlation(6b):

4.8% - single, randomly chosen

3.1% - sequence of test images

conclusion
Conclusion…
  • A fast face localization and verification based on a robust form of correlation.
  • Optimization: random sampling speed the evaluation of correlation 25 times real time.
  • Recognition: Optimized Robust Correlation outperformed the two standard techniques.
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