1. Face Recognition
By Sunny Tang

2. Outline Introduction Requirements Eigenface Fisherface
Elastic bunch graph
Comparison

3. Introduction What is face recognition? Applications
Security applications
Image search engine

4. Requirements
Accurate
Efficient
Light invariant
Rotation invariant

5. Eigenface Euclidean distance between images
Principal component analysis (PCA)
For training set T1, T2, …… TM
Average face ψ = 1/MΣ TM
Difference vector φi = Ti – ψ
Covariance matrix C = 1/MΣ φn φTn

6. PCA

7. Recognition Projection in Eigenface Projection ωi = W (T – ψ)
W = {eigenvectors}
Compare projections

8. Fisherface Similar approach to Eigerface PCA
Fisher’s Linear Discriminant (FLD)
PCA
Scatter Matrix
Projection Matrix

9. Fisherface FLD Between-class scatter matrix
Within-class scatter matrix
Projection Matrix

10. FLD

11. Elastic Bunch Graph
Gabor wavelet decomposition
Gabor kernels

12. Gabor Filters

13. Jets
Small patch gray values
Wavelet transform

14. Comparing Jets
Amplitude similarity
Phase similarity

15. Comparing Jets

16. Face Bunch Graphs (FBG)
Stack like general representation
Two types of FBG:
Normalization stage
Graph extraction stage
Graph similarity function

17. Graph Extraction Step 1: find approximate face position
Step 2: refine position and size
Step 3: refine size and find aspect ratio
Step 4: local distortion

18. Recognition
Comparing image graph
Recognized for highest similarity

19. Comparison Eigenface Fisherface Elastic bunch graph
Fast, easy implementation
Fisherface
Light invariant, better classification
Elastic bunch graph
Rotation, light, scale invariant

20. Q & A Section