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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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