Face Recognition By Sunny Tang.

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Presentation transcript:

Face Recognition By Sunny Tang

Outline Introduction Requirements Eigenface Fisherface Elastic bunch graph Comparison

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

Requirements Accurate Efficient Light invariant Rotation invariant

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

PCA

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

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

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

FLD

Elastic Bunch Graph Gabor wavelet decomposition Gabor kernels

Gabor Filters

Jets Small patch gray values Wavelet transform

Comparing Jets Amplitude similarity Phase similarity

Comparing Jets

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

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

Recognition Comparing image graph Recognized for highest similarity

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

Q & A Section