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Synthetic Data for Face Recognition

Synthetic Data for Face Recognition. CS525 Vijay Iyer. Face Databases. Current databases (CMU PIE, FRGC/FRVT, FERET) Short range Indoors Artificial Light Only one known attempt at creating long range outdoor database CMU PIE small but very controlled dataset

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Synthetic Data for Face Recognition

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  1. Synthetic Data for Face Recognition CS525 Vijay Iyer

  2. Face Databases • Current databases (CMU PIE, FRGC/FRVT, FERET) • Short range • Indoors • Artificial Light • Only one known attempt at creating long range outdoor database • CMU PIE small but very controlled dataset • FERET , FRGC/FRVT large but sacrifice control • We need more databases to further face recognition

  3. Why Synthetic? • Long term cost is cheaper(still costly so this is not a deciding factor) • More experimental control • Explore more conditions • Can also be used to validate changes in systems

  4. 4DPhotohead Framework • Custom Display Software • Allows for simple scripted animation • 3D Models • Generate models from CMU PIE • Created with AnimetricsForensica software • Custom Display Hardware • High power projector (3000 lumens) • Cover blocks out light to improve visibility

  5. 4D Photohead Software

  6. Animetrics FaceGen Model Validation 100% 47.76%

  7. Capture/Display Hardware

  8. Initial Results

  9. Summary/Conclusions • Created an end to end framework which is validated to work with frontal poses • Scientifically validated that the models facing forward are equivalent to human beings for ROI of face recognition • Shown how synthetic data takes out or controls many existing variables in facial recognition. • Recent publication in the upcoming AMFG workshop shows the biometric research community has interest in developing this technique further.

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