1. Learning Gender with Support Faces
6.899 Learning & Inference in Vision
MIT AI Laboratory
April
Baback Moghaddam

2. 6.899 Learning & Inference in Vision
Kernel Sex Machines
6.899 Learning & Inference in Vision
MIT AI Laboratory
April
Baback Moghaddam

3. Overview Why Gender? Human Perception Computational Studies
Man/Machine Evaluation
Conclusion

4. Face Publications by Category (from F&G’95/96/98/2000)

5. Applications of Face Gender
HCI (agents interacting with strangers/public)
Demographics
Consumer Statistics & Profiling!
“How many women entered this store today?”
Security (“smart buildings”)
Hybrid Classifiers (fast/robust recognition)

6. Gender-Based Recognition?
Females
Males

7. Face Perception in Humans
Cortical localization in IT/STS [Desimone et al., 1984]
Independent face modules [Bruce et al. 1986]
Expression
Gender
Race
Age
Familiarity
Identity

8. Human Gender Perception
Accuracy: Error < 5%
with high-res photographs [Valentin & Endo 1992]
even with hairstyle minimized [Bruce et al. 1993]
increases to 22% without shape info
Mechanisms
Prototypes or “schemas” [Goldstein & Chance 1980]
Feature-based or “configural” [Roberts & Bruce 1988]

9. Gender Prototypes
Images courtesy of University of St. Andrews Perception Laboratory

10. Gender Prototypes
Images courtesy of University of St. Andrews Perception Laboratory

11. Computational Studies

12. Classifier Evaluation
Compare “standard” classifiers
1755 FERET faces
80-by-40 full-resolution
21-by-12 “thumbnails”
5-fold Cross-Validation testing
Compare with human subjects

13. Automatic Face Processor
[Moghaddam & Pentland, PAMI-19:7]

14. Gender (Binary) Classifier

15. Binary Classifiers NN
Linear
Fisher
Quadratic
RBF
SVM

16. “Support Faces”

17. Classifier Performance

18. Classifier Error Rates

19. Gender Perception Study
Mixture: 22 males, 8 females
Age: mid-20s to mid-40s
Stimuli: 254 faces (randomized)
low-resolution 21-by-12
high-resolution 84-by-48
Task: classify gender (M or F)
forced-choice
no time constraints

20. Human Performance Stimuli Results s = 3.7% 84 x 48 21 x 12 N = 4032

21. Machine vs. Humans
% Error

23. Conclusions Support Vector Learning
requires storage of ~20% of data
identifies critical “support faces” (features?)
low complexity, fast computation
superior performance
High accuracy with very low-resolution
Gender modules for HCI/Biometrics/etc.