1. Dynamic Face Recognition Committee Machine Presented by Sunny Tang

2. Outline  Introduction  Previous Work  Dynamic Architecture  Face Verification System  Conclusion & Future Work

3. Committee Machine  Train a committee of estimators and combine the individual predictions  Motivation –Achieve better performance –Reduce computational complexity  Type of Committee Machine –Static structure –Dynamic structure

4. Static Structure  Ignore input signals  Fixed weights  Examples –Majority Voting –Ensemble averaging –Bagging

5. Dynamic Structure  Employ input signal to improve the classifiers  Variable weights  Examples –Gating networks –Hierarchical mixture of experts

6. Previous Work  Static Face Recognition Committee Machine consist of 5 experts

7. Drawback  Expert weighting depends on overall performance of a particular face database  Weight is fixed once the system is trained  Only frontal faces are used for identification / verification

8. Dynamic Architecture  Keep performance of experts on different face databases  Gating Network consisting of a neural network to determine which performance to use as weight Database# Image ORL400 Yale165 CVL800 Umist560 HRL1370 Feret1200

9. Dynamic Architecture  Gating Network –Input: image x –Output: –P x : Performance for x’s database Gating Network x g1g1 g2g2 Expert Network y1y1 Expert Network y2y2  xx r

10. Face Verification System  Biometric Security Application –Personal authentication  Target –Low false acceptance –Low false rejection  Two face images are used –Frontal –Profile  Hierarchical Structure

11. System Snapshot

12. Hierarchical Dynamic Architecture r x2x2 x2x2 Ensemble Network r1r1 Gating Network Expert Network Expert Network y 11 x1x1 x1x1 x1x1 g 11 g 21 y 12 Frontal Face r2r2 Gating Network Expert Network Expert Network g 22 g 12 x2x2 y 21 y 22 Profile Face

13. Face Verification System  Accept when –Both frontal and profile results match the claimed identity –Each committee machine has overall confidence over a selected threshold  Feedback Mechanism –Adjust individual expert’s weight –Update corresponding performance

14. Conclusion & Future Work  Conclusion –We propose a framework for a dynamic committee machine –We design a face verification system for security purpose  Future Work –Work on the system and get experimental result