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 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

Fast, easy implementation Fisherface

Light invariant, better classification Elastic bunch graph

Rotation, light, scale invariant

Q & A Section