1. 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. 4D Photohead Framework Custom Display Software Allows for simple scripted animation 3D Models Generate models from CMU PIE Created with Animetrics Forensica software Custom Display Hardware High power projector (3000 lumens) Cover blocks out light to improve visibility

5. 4D Photohead Software

6. Model Validation 100% AnimetricsFaceGen 47.76%

7. Capture/Display Hardware

8. Initial Results DataSetIsoDistanceV1Comm. FRGC Screen ShotsN/A 42.11- FaceGenScreenSho tsN/A 47.76- AnimetricsScreenS hotsN/A 100- PIE-3D-20100210B50081M100- PIE-3D-20100224A125214M58.82100 PIE-3D-20100224B125214M45.59100 PIE-3D-20100224C250214M81.82100 PIE-3D-20100224D400214M79.1100 Securics-1- 02242010125214M20100 Securics-2- 02242010250214M33.33100 Securics-3- 02242010400214M30100

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.