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Face Recognition By Sunny Tang.

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Presentation on theme: "Face Recognition By Sunny Tang."— Presentation transcript:

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


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

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