Face Recognition & Biometric Systems, 2005/2006 Face recognition process.
Synthetic Data for Face Recognition
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Transcript of Synthetic Data for Face Recognition
Synthetic Data for Face Recognition
CS525Vijay Iyer
Face DatabasesCurrent databases (CMU PIE,
FRGC/FRVT, FERET)Short rangeIndoorsArtificial Light
Only one known attempt at creating long range outdoor database
CMU PIE small but very controlled datasetFERET , FRGC/FRVT large but sacrifice controlWe need more databases to further face
recognition
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
4D Photohead FrameworkCustom Display Software
Allows for simple scripted animation3D Models
Generate models from CMU PIECreated with Animetrics Forensica software
Custom Display HardwareHigh power projector (3000 lumens)Cover blocks out light to improve visibility
4D Photohead Software
Model Validation
100%
Animetrics FaceGen
47.76%
Capture/Display Hardware
Initial ResultsDataSet Iso Distance V1 Comm.
FRGC Screen Shots N/A N/A 42.11 -FaceGenScreenShots N/A N/A 47.76 -AnimetricsScreenShots N/A N/A 100 -
PIE-3D-20100210B 500 81M 100 -
PIE-3D-20100224A 125 214M 58.82 100
PIE-3D-20100224B 125 214M 45.59 100
PIE-3D-20100224C 250 214M 81.82 100
PIE-3D-20100224D 400 214M 79.1 100Securics-1-02242010 125 214M 20 100Securics-2-02242010 250 214M 33.33 100Securics-3-02242010 400 214M 30 100
Summary/ConclusionsCreated an end to end framework which is
validated to work with frontal posesScientifically 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.