1. Exploring Gradient-based Face Navigation Interfaces Tzu-Pei Grace Chen Sidney Fels Human Communication Technologies Laboratory Department of Electrical and Computer Engineering University of British Columbia

2. Presentation outline Brief introduction Video introduction Design Demonstration of face navigation interfaces Findings and implications

3. Motivation Sanjusangen-do (1,001 Buddha statues temple)

5. Background: Alternate methods 1. Simplified Parametric methods [DiPaola 2002 and DeCarlo et al. 1998] –Humanoid vs. realistic-looking faces

6. 2. Feature-based Method facial features instead of whole face –CAFIIR – Wu et al. 1994, SpotIt! – Brunelli and Mich 1996 Works well for distinctive faces but not normal faces Verbal description encouraged [Valentine 1991] “Overshadowing” effect on memory [Schooler and Engstler 1990] Facial categories can be large requiring a large feature library

7. 3. Holistic Methods Draw from statistical methods for face retrieval –Eigenface -Turk and Pentland 1991 Variation created for creating holistic face space –FaceGen [Blanz and Vetter 1999]

8. The Face Space from FaceGen [Blanz and Vetter 1999] 128 principal components axes Every position defines a face Average face is at the origin The further from the origin, the more caricature-like the faces Like Valentine’s norm based face space –[Valentine, 1991]

9. How do we make navigation easy in this space? 1. Finding an object successfully is in knowing where and how to look for it. 2. People have acute face recognition skills.

10. Well-structured Space

11. Face Gradients

12. The Interfaces Three interfaces –Static Sliders –Dynamic Sliders –Wheel

13. Static Sliders beforeafter

14. The Dynamic Sliders beforeafter

15. Wheel

16. User Testing Variables: resolution, interface and target Within subject factorial design 15 subjects Task: face matching Record navigation patterns

17. Findings Static Sliders: trial and error behavior Wheel: local refinement Dynamic Sliders: global access

18. Implications Gradient-based mechanism is suitable for refinement in face matching tasks Hybrid system of interfaces useful for tackling high dimensionality of faces and avoids verbal categorization (labeling) Principle should apply to other face synthesizing system that generates gradients

19. System Limitations Resolution must be above users’ just noticeable difference (JND) Selection of 128 principal components Screen Real Estate

20. Future Work Increase the number of navigation axes and the rounds of neighbors displayed to optimally balance –Screen real estate –Usability –Run time performance Investigate parameters –Starting position of navigation –Users’ just noticeable difference Qualitative study of finding faces not explicitly represented in the system

21. Summary Presentation: overview, design principles, demonstrations and findings Gradient-based mechanism suitable for refinement Hybrid system of interfaces useful for tackling high dimensionality of face

22. Face-related research is expanding but relatively little work is done on user interface techniques for supporting face navigation. Our research is taking that step.

23. The End Human Communication Technologies Lab http://hct.ece.ubc.ca Face Navigation Game http://www.ece.ubc.ca/~tzupei/facenav.htm