FaceRec Tutorial

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Challenges in Face Recognition

Biometrics

Sujeewa AlwisCybula Ltd


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Why use face?

Every one has got a fairly unique face

Can be captured without user cooperation (passive)


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Application Modes

Verification Are you the same person you say you are?

System captures a new biometric sample and the person submits an ID.

Yes/no answer indicates authentication result.

Identification Who are you?

System captures a new biometric sample. It does a database search and presents

the top n similar matches may need a human operator to make the final decision.

Watch-list Are we looking for you

System captures a new biometric sample. System triggers an alarm only if that

person is in the database. Similar to identification - but uses an additional threshold

to identify a hit.


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Iris

Advantages

highly unique (five differentpatterns in even two identical twins)

Stable after the first year of birth

Disadvantages

Need user cooperation

Difficulties during enrolment

The most successful technique is based on projecting Iris pattern onto a

Gabor wavelet (Daugman, 1993). Gabor coefficients represent the biometric

template - commercialised by Iridian technologies


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Fingerprints

Advantages

Availability of large fingerprint databases

Disadvantages

Associated with crime control/investigation

Need user cooperation

Need to keep the capture surface clean

and germ-free not suitable for high-throughput applications

Represents minutiae points in a map

Cross match technologies is one of the companies that sell fingerprint recognition

systems


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Gait recognition

Palm print recognition

Voice recognition


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Combinations

Face + Iris (Wang, 2003)

Face + Ear (Chang, 2003)

Face + Gait (Shakhnanorvich, 2002)

Face + Palm print + Fingerprint (Ross, 2001)

Face + Voice + Lip movement (Frischholz, 2000)

Face + Voice (Kittler, 1997)


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Face Representation 2D vs. 3D

Advantages

Can deal with pose

variations if the cameras

can capture the full face

Less sensitive to lighting

variations

Better accuracy duringrecognition (Experimental

results from Notre Dame

University, Chang et al.

2003)

Advantages

Availability of large 2D

image collections

Capture devices are

currently cheaper

3D2D


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Face Representation 2D vs. 3D (contd.)

DisadvantagesCameras are still expensive

Takes time to reconstruct

models

Unavailability of large

collections of 3D data

(UofY/ Cybula data set, U ofNotre Dame data set)

DisadvantagesCannot handle pose

variations

Sensitive to lightingvariations, shadows etc.

3D2D


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Techniques

Model basedTechniques

Active appearance/

shape models,

Fitting morphable models

Appearance basedtechniques

Eigen faces

and Fisher faces

Feature basedTechniques

Distances between landmark

points such as eyes,

nose and mouth.

Graph matching techniques


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Eigen Analysis

One of the most popular methods for face recognition

The central argument is

faces contain a lot of features some are common to all faces, some are

highly discriminatory information.

So they have to be mapped to different feature space that consists of

discriminatory information a dimensionality reduction method is needed

Eigen analysis provides a way to identify dimensions that indicate high

variance - so we can use Eigen analysis to extract principal components


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A simple example

y = P x

where

y coordinates in the new

space

x coordinates in the

previous space

P projection matrix

- a face


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Eigen Faces

projections of a face template along different principle components


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Previous Work

Using 2D images

Sirovich and Kirby (1987), Turk and Pentland (1991) Using 3D images

Heseltine, Pears and Austin (2003), Chang, Bowyer and Flynn (2003)


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Linear Discriminant Analysis

The aim is to minimise the

within class separation and

maximise between class

separation. In other words,

maximise the ratio between

between class variance andwithin class variance

Maximise (SBSw-1

)Where

SB between class scatter matrix

Sw within class scatter matrix

Subject A Subject B

Subject C


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Previous Work

Using 2D images

Belhumeur, Hespanha and Kriegman (1997), Etemad and Chellappa(1996), Liu and Wiechsler (1998), Kittler (1999)

Using 3D images

Heseltine, Pears and Austin (2004)


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Is LDA always better than PCA?

PCA

LDA

D PCA

D LDA Martinez and Kak

(IEEE PAMI, 2001)Present experimental

data to show that

LDA does not always

outperform

PCA particularly when

the number of samples

in a class is small


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Feature based matching techniques

One of the earliest techniques is to use distance between landmarks such

as eye, nose and mouth

This technique may not be robust due to pose variations and it may be

difficult to accurately identifying the required feature points


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Cybula approach

3D graph matching

A 3D mesh is used to identify a set of significant points

we identify high curvature points on face profiles

These points and the relationships between points are

represented in a graph

A graph matching framework called Relaxation by Elimination (RBE)developed at York is used.


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Elastic Bunch Graph Matching

But we are not the only people who have applied graph matching to faces!

Wiskott, Fellous, Kruger and Malsburg (1999) have used graph matching

for 2D face recognition.

Each landmark point (eyes, mouth et.) is represented by a stack of waveletresponses. They become the nodes of the graph. Distances are

represented in edges.

Graph for a new image can be fitted by scaling, rotating and translating astandard model graph.

Dissimilarity measure is a straight-forward comparison between graphs


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Model based recognition

Active appearance models (Cootes, Edwards and Taylor, 2001)

A statistical appearance model is constructed by combining a shape model

and a texture model.

Shape model is constructed by identifying the positions of landmark points

Texture model represent gray level intensities.

Model parameters are identified by applying Eigen analysis.

Recognition is an iterative process in which model parameters are adjusted

to obtain the best match


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3D morphable model (Blanz and Vetter, 2003)

A set of laser scanned 3D image models (100 males and 100 females) are used to

construct the morphable 3D model. Shape is represented by 3D co-ordinates while

texture is represented by colour.

Model parameters are calculated by applying Eigen analysis.

3D model is deformed to obtain the best fit between its 2D projection and the new2D image. New model parameters are used to describe the new image. So this couldbe seen as 2D to 3D mapping

Optimisation process involves finding out optimum values for model parameters aswell as scene parameters (pose, focal length of the camera, light intensity, colour anddirection)


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One remaining issue how to keep the data collections

updated?

Face is changed when people become older and it could depend on both

internal and external factors

Lanitis, Taylor and Cootes (2002) have extended their work on active

appearance model to predict the age of an unseen subject and then to

simulate/ eliminate age effects

Using training data, they build up a weighted person specific aging function

to predict an age of a person using appearance as well as external factors

such as lifestyle

Age simulation can be done by changing the model parameters.


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Evaluation

False acceptance rate (FAR) number of times a wrong person is accepted

False rejection rate (FRR) - number of times the correct person is rejected

Equal error rates the value that FAR and FRR becomes equal

Time to verify

Time to capture/ enrol


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Benchmark Assessments

FRVT has been replaced by the Grand Challenge Experiment led by NIST

First round was finished in this month the second round resultssubmission is due next year


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FaceRec Tutorial

Documents

Transcript of FaceRec Tutorial