An Associate-Predict Model for Face

Recognition

CVPR 2011

Qi Yin1,3 Xiaoou Tang1,2 Jian Sun3

1. Department of Information EngineeringThe Chinese University of Hong Kong

2. Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences, China

3. Microsoft Research Asia

Outline

Introduction

Identity Data Set and Face Representation

Associate-Predict model

Switching Mechanism

Experimental Results

Introduction

Appearance-based for face recognition

Inevitable obstacle

Associate-Predict model

The studies of brain theories

Introduction

Identity Data Set and Face Representation

Identity data set

Face representation

Identity data set

200 identities from the Multi-PIE data set

7 pose

4 illumination

Face representation

Representation at the facial component level

12 facial components

Face F = (f1, f2, ..., f12) › fi for each component

Associate-Predict model

Appearance-prediction model

Likelihood-prediction model

Appearance-predictionmodel

Two input faces

Setting : SA , SB › A and B are facial components

Select the specific face image setting is equal to SB

› component A’ from this image

Appearance-predictionmodel

dA = |fA' − fB|› distance between the components

dB = |fB' − fA|

Final distance between A and B: 1/2 (dA + dB)

Appearance-predictionmodel

Adaptive distance dp

αA and αB : weight

After the “appearance-prediction” on all 12 facial components , we can obtain a new composite face

Likelihood-prediction model

Using classifier measure the likelihood of B belonging to A

Positive training samples› Input face › the K most alike generic identities

Switching Mechanism

Implement this switching mechanism› facial components : A and B › settings : SA = { PA , LA } and SB = { PB , LB }

Categorize the input pair into two classes› “comparable” › “not comparable” › based on the difference of SA and SB

Switching Mechanism

Comparable class › {|PA − PB| < 3 } and {|LA − LB| < 3 }

Not comparable class › the rest situations

Switching Mechanism

The final matching distance dsw

› da : the direct appearance matching › dp : the associate-predict model

Experimental Results

Experiments on the Multi-PIE and LFW data sets

Basic comparisons

Results on benchmarks

Basic comparisons

Holistic vs. Component

Basic comparisons

Positive sample size› number of positive samples is 1 +

28*k › “1” is the input sample › K is the selected number of top-

alike associated identities

Basic comparisons

K = 3 as the default parameter

Basic comparisons

Switching mechanism› the switch model can effectively improve

the results on both benchmark

Results on benchmarks

Multi-PIE benchmark

LFW benchmark

Multi-PIE Benchmark

LFW benchmark