Face Biometrics – Systems, Applications and Experiments

1. Face Biometrics – Systems,Applications and Experiments (Team 1)Jackie Abbazio, Sasha Perez, Denise Silva and Robert Tesoriero (Team 2) Faune Hughes, Daniel Lichter, Richard Oswald and Michael Whitfield Clients: Fred Penna and Robert Zack

2. Project Overview • Literature Review • Facial Anthropometrics • The orbital (eye) region of 13 candidates was studied and analyzed. • Software Selection: • Neurotechnology’s Verilook, Luxand’s FaceSDK, 360 Degree Web’s FACE and others. • Conduct Experiments with a focus on Security and Aging.

3. Introduction

4. Teams Team 1 Primary Objectives: • Conduct technology reviews of selected facial recognition software. • Select several facial recognition applications for team 2 to perform experiments. • Review the state of Facial Biometric Technologies.

5. Teams Team 2 Primary Objectives: • Experiments • Evaluate enrollment, data collection, feature extraction, classification, ease of use, performance, and other capabilities.

6. Objectives

7. Objectives Primary Objectives of the Study: Software Security Aging Applications Strengths and Weaknesses Costs, Benefits and Limitations

8. Technology Review • 2D vs. 3D • Barriers and Obstacles • Emerging Technologies • False Acceptance Rates (FAR) • False Rejection Rates (FRR)

9. Anthropometrics

10. Anthropometrics • Anthropometry is the study and measurement of human physical dimensions • Pioneer in Anthropometry: Dr. Leslie Farkas • Her defined “landmarks” prove that every face had different measurements

11. Landmarks

12. Facial Measurements - Orbital • It is believed that the eye region does not change much over time. • We measured the orbital region of each photo which consist of both the biocular distance and the intercanthal distance.

13. Orbital Measurements

14. LuxandFaceSDK Example Enrolled into class database This 1979 image matched with 2007 image 2008 image with 51% similarity

15. LuxandFaceSDK Similarity Matrix

16. VeriLook 3.2 • Designed for biometric system developers and integrators. • Allows for easy integration and rapid development of biometric applications using functionality. • Can perform simultaneous multiple face detections with the ability to process 100,000 faces per second and it recommends the minimum image size to be 640x480 pixels .

17. VeriLook Aging Result The results show that the photo from 1969 matched a photo from 2008 with a similarity score of 18 or 10%. This result is comparable with the FaceSDK age identification test, where the same image from 1969 matched the same photo from 2008 with a 61.9% similarity rate.

18. Luxand – VeriLook Comparison VeriLook Identification and Authentication Results FaceSDK Identification and Authentication Results

19. Test Samples Photo Database • 44 photos from 19 subjects • Digitized through webcam, digital camera, or scanner

20. 360 Degree Web’s Face

21. Experimental Results

22. FAR Threshold Experiment

23. Photo Environment • Attributes of the photo and purpose for which it was taken

24. Identification Experiments

25. Gender and Ethnicity

26. Enrollment with Generalization • Unique to Verilook • Combines facial templates from multiple photos to give better matches

27. Conclusions • All products tested have strengths and weaknesses. None suitable for security applications. • Verilook merges all photos of single person into one; better matching • Limited than Luxand. Only allows enrollment of high-res images. • Did not perform as well as FaceSDK software • Luxand • works very well in identifying face similarity among people in a group • worked relatively well matching aged images • Future Work - Emerging Technologies