An associate predict model for face recognition
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An Associate-Predict Model for Face Recognition. CVPR 2011 Qi Yin 1,3 Xiaoou Tang 1,2 Jian Sun 3 1. Department of Information Engineering The Chinese University of Hong Kong 2. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, China 3. Microsoft Research Asia.

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An Associate-Predict Model for Face Recognition

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An associate predict model for face recognition

An Associate-Predict Model for Face Recognition

CVPR 2011

Qi Yin1,3Xiaoou Tang1,2Jian Sun3

1. Department of Information Engineering

The Chinese University of Hong Kong

2. Shenzhen Institutes of Advanced Technology

Chinese Academy of Sciences, China

3. Microsoft Research Asia


Outline

Outline

  • Introduction

  • Identity Data Set and Face Representation

  • Associate-Predict model

  • Switching Mechanism

  • Experimental Results


Introduction

Introduction

  • Appearance-based for face recognition

  • Inevitable obstacle

  • Associate-Predict model

  • The studies of brain theories


Introduction1

Introduction


Identity data set and face representation

Identity Data Set and Face Representation

  • Identity data set

  • Face representation


Identity data set

Identity data set

  • 200 identities from the Multi-PIE data set

  • 7 pose

  • 4 illumination


Face representation

Face representation

  • Representation at the facial component level

  • 12 facial components

  • Face F = (f1, f2, ..., f12)

    • fi for each component


Associate predict model

Associate-Predict model

  • Appearance-prediction model

  • Likelihood-prediction model


Appearance 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 prediction model1

Appearance-predictionmodel

  • dA = |fA' − fB|

    • distance between the components

  • dB= |fB' − fA|

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


Appearance prediction model2

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

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

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 SAand SB


Switching mechanism1

Switching Mechanism

  • Comparable class

    • {|PA − PB| < 3 } and {|LA − LB| < 3 }

  • Not comparable class

    • the rest situations


Switching mechanism2

Switching Mechanism

  • The final matching distance dsw

    • da: the direct appearance matching

    • dp: the associate-predict model


Experimental results

Experimental Results

  • Experiments on the Multi-PIE and LFW data sets

  • Basic comparisons

  • Results on benchmarks


Basic comparisons

Basic comparisons

  • Holistic vs. Component


Basic comparisons1

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 comparisons2

Basic comparisons

  • K = 3 as the default parameter


Basic comparisons3

Basic comparisons

  • Switching mechanism

    • the switch model can effectively improve the results on both benchmark


Results on benchmarks

Results on benchmarks

  • Multi-PIE benchmark

  • LFW benchmark


Multi pie benchmark

Multi-PIE Benchmark


Lfw benchmark

LFW benchmark


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