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Transcript of Facial Recognition 2
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3
Introduction
For obvious reasons, the human brain and visual organs can be considered the best existing
ace recognition machine – ever – and orever. A specic area o the human brain called usiorm ace
area (FFA) has been proven to be totally dedicated to this task.
Because ace recognition is the most natural thing or any human being, acial recognition would
appear to be the most natural o biometric techniques. Human ace recognition is the most widely
used way o identication or authentication o identity, and accounts or the presence o portraits on
most o the identication documents that we carry in our purses and wallets, be it an ID card, driver
license, credit card, library card or gym club card.This apparent ease o use is the cause or many antasies, inconsistencies and diculties while
implementing automatic ace recognition systems in the eld. Because the technology has an
outstanding competitor : the human brain, trained rom birth to somehow do the exact same thing
There are many applications. Automatic acial recognition is actually used in civil spheres in order to
guarantee the unique nature o identity documents and in military or law enorcement applications,
since the human ace easily leaves “traces” when crimes are recorded by CCTV cameras or the
cameras o witnesses.
The acquisition o portrait images is simple, contactless and does not require any highly specic
equipment; a act that acilitates the implementation o automatic acial recognition. Rapid advances
are being made in acial recognition technology, and it thus has every trait expected o a majorbiometric technique, up to the point where it is on the verge o taking on its greatest challenge: beat
the human brain, best ace recognition machine ever, but not orever. Still, in order to deliver its ull
eciency, a number o caveats have to be taken into account during implementation.
In an eort to answer the increasing number o questions being put orward, Morpho has taken
stock o acial recognition in general, its use, the state o the art o the technology and its technical
and commercial potential.
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A brie background
The origins
From the very advent o photography, both government
agencies and private organizations have kept collections
o portraits and ID photos have gradually made their
way onto all personal identication documents, rom
the most ocial passports to inormal membership
cards issued by sports clubs.
Beore the use o computers to recognize aces was
even considered a possibility, acial recognition was
already the subject o a great deal o research. Examples
include:
• the development o identication parade or “line-
up”(1) techniques in the United Kingdom, in which a
witness is conronted with a group o physically similar
people, one o whom is a suspect. The witness must
decide whether one o the persons in the group was
present at the scene o the crime.
• the work done by Bertillon on ace classication. In
order to recognize delinquents who are repeatedly
arrested, without having to resort to large collections
o portraits, Bertillon suggested that the portraits be
sorted by common morphological characteristics, i.e.
the specic shapes o the dierent parts o the ace.
This classication is known as the “spoken portrait”.
Facial recognition with goodquality portraits
The rst attempts to automate acial recognition started
in the 1960s in semi-automatic mode. They essentially
consisted in checking the coherence o measurements
between dierent characteristic points o the ace (e.g.
the corners o the eyes, the hairline, etc.). They were not
very successul, because aces are by nature very mobileand measurements between characteristic points are
aected by orientation, to the extent that specially-
developed models quickly proved to be necessary.
At the end o the 1980s, the development o the
eigenaces(2) technique prompted a more intense
research eort. This technique is used to nd a ace in
a photo and to compare images o aces. Researchers
quickly ound that the overall issue o acial recognition
was complex, but could be simplied by only taking
into consideration portraits that are coherent in terms
o orientation, lighting, expression and image quality.
Research ocused on this problem, the ICAO* dened
criteria to obtain controlled portraits and meaningul
test sets were created.
At the start o 2007, the NIST* published the results o
its “FRVT 2006”* test. Its conclusions were quite clear.
Research had reached a point where the operational use
o acial recognition on high-resolution rontal images
taken in a controlled environment was now easible. But
this event obviously did not put an end to work on the
recognition o controlled portraits. More improvements
are expected, but acial recognition has thus become a
biometric technique in its own right.
(1) Alphonse Bertillon, 1853-1914, criminologist who developed
judicial anthropometry in France.
(2) Eigenaces: a acial recognition technique that consists in learning
the distinctive characteristics o aces rom a broad sample o
portraits using each complete image rather than local characteristics
(e.g. the eyes, nose or mouth).
Figure 1: Portrait parlé “class”. Source Library o Congress, USA.
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General facial recognition
Since 2007, research has been looking into signicantly
more dicult problems, in which aces are not viewed
rontally, resolution is low or the image quality is
sometimes poor. With the MBGC*, the NIST is again
seeking to assess perormance and has provided
researchers with representative data (images and videos
o aces under non-controlled conditions). It is thestart o a new era and we can expect to see signicant
progress over the coming years.
Figure 2: Eigenaces, courtesy o Santiago Serrano Drexel
University, USA.
Figure 3: Facial recognition history.
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The Applications
Automatic acial recognition is a orm o biometrics. It is used or authentication
(checking that a person really is who they say they are) and identication (nding
out who someone is rom a group o known persons).
Like most biometric techniques, acial recognition has applications in the policing
and civil elds and or access control. Facial recognition is special due to the portraits
themselves, which are widely available and easy to acquire. Their use is acceptable
to the general public.
Criminal Justice
Identifcation and maintenance o aportrait reerence databaseJust like automatic ingerprint recognition, acial
recognition allows police orces to manage the les o
people o interest by making sure that there are not
several dierent records or a single person. While
this task is already perormed using ngerprints, acial
recognition provides more benets:
• it increases population coverage o the identication
scheme, enabling identication o individuals whose
ngerprints cannot be acquired or various reasons
• by combining the two biometric modalities, superior
identication perormance can be achieved, thereore
reducing the workload involved in the verication
process
The Pierce County Sheri’s Oce in Washington, USA,
demonstrated the high precision o the automatic acial
identication o suspects and that identication is
possible without calling on ngerprint experts.
Identity checks in the feldWith just a camera and suitable means o transmission,
it is possible to check the identity o a person in the
eld using a photograph o their ace. Police ocers
equipped with PDAs can submit search requests to
remote acial recognition systems and quickly determine
whether an individual is already known to the orces o
law and order.
ID checks can be carried out on just the ace or both the
ngerprints and the ace, i the ocer has the equipment
required to take ngerprints. The combination o the
two biometric techniques increases the precision o
searches and allows reliable, automatic decisions to
be sent to the eld, without the ocer requiring any
expertise in ngerprints.
6
Figure 4: mobile
acial recognition.
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Criminal investigations and inormationImages are oten made available or inquiries. They
may come rom surveillance videos, a witness’s camera,
Internet sites or copies o identity papers. These images
may show the ace o a suspect.
To begin with, the portraits must be extracted rom the
available evidence. In some investigations, hundreds o
hours o video ootage are analyzed and the “manual”search or excerpts in which aces are visible is a long
and painstaking job. It is the reason why automatic
assistance is necessary. Current automatic ace extraction
techniques work well with almost ull rontal views o
aces and when the quality o the video is good enough.
Research is currently being made into the extraction o
side and three-quarter views o aces.
Even i the quality o the extracted portraits is highly
variable, it is still possible to compare them with portraits
o persons who are known to the police. Morpho’s
experience in this eld shows that these searches can
already solve and correlate crimes. Our French, American
and Australian customers have scored numerous hits
with high-quality images, such as authentic or alse ID
documents or images posted on the Internet. It is also
interesting to note that certain criminal cases have been
solved using low quality images.
Operational examples o the use o surveillance videos
do exist, but they are rarer. By way o example, images
o raudulent use o ATMs or assaults close to an ATM
can be used to solve crimes i the camera obtains well-lit,
acial images. However, they cannot be used to
successully close investigations i the lm only shows
the top o the suspect’s head or i the images are
blurred. The combined advances o video surveillance
systems and acial recognition technology should
enable more crimes to be solved using video data in
the next ew years.
Another source o acial images is the acial composite
picture. I the recollections o the victim or the witnessesare precise enough to make a acial composite picture
resembling the oender, then investigation by acial
recognition may lead to success.
PreventionFacial recognition can also be used or preventive
purposes. It can be used to search or precedents.
By way o example, i a le o pedophiles is available,
then ID photos can be used to check whether people
who work with children are in the le.
In some cases, acial recognition can also be used to
interactively locate persons wanted by the police in
video ootage. This application is subject to controversy,
since it is oten considered to inringe civil liberties. In
any case, it is not currently suited to cases in which a
very small number o persons need to be identied in a
crowd. Even i this technique were to reach the excellent
accuracy level o 90% o persons actually ound with
just 0.1% alse alarms, looking or one person amongst
a crowd o 100,000 passing people per day would
operationally generate 100 alse alarms per day. This
would have a negative impact on the vigilance o
control operators.
Figure 5: acial
recognition
in a crowd.
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On the other hand, interactive acial recognition is
already possible in controlled passages. By way o
example, when travelers approach the border police
or a travel document check using acial biometry, they
must be cooperative and show their ace. In this case,
it is quite easy to check the travel documents and make
a comparison with the lists o wanted persons. The
operators who check passport control processes can also
check any alerts received in response to these searches.
Civil applications, access controlsand border controls
Issuance o identity documentsFacial recognition is particularly well suited to checks
o the uniqueness o application or identity papers.
In a non-criminal context, it is quite normal to provide
a photo, while ngerprints will always have criminal
connotations and it is more dicult to acquire an image
o an iris than o a ace.
By way o example, driving licenses include a photo o
the holder, but rarely include any other biometric data.
As a consequence, acial recognition applications can be
used to guarantee that a single motorist cannot possess
several driving licenses. Morpho developed a solution
or this very purpose or the state o New South Wales
in Australia.
With regard to travel documents, the ICAO has
recommended that the portrait should be the only
compulsory biometric record.
Control o identity documentsOnce the documents have been issued, acial recognition
can be used to check that they are indeed being used
by their legitimate holders. This check can be made
by simply lming the holders when they present their
documents. By way o example, the SmartGates*
or automatic passport checks deployed by Morpho
have accelerated border ormalities in Australian airports.
Today, document holders are required to stand still in
ront o the camera, but in the near uture the check will
be made as they pass through the checkpoint. Morpho’s
rapid and robust Face on the Fly* technology is capable
o acquiring aces in three dimensions, without requiring
the subject to stand still.
8
Figure 6: acial recognition at airport. Figure 7: biometric passport.
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This application is so easy to use that a broad range o
usages is possible. For example, it would be easy or
universities to check the identity o students when they
arrive to take an exam (authentication checks).
Access controlThe purpose o access control is to check that anyone
attempting to access a secure zone is entitled to do so.
Access controls are made in the same way as ID checks.They are very easy or the user i acial recognition is used.
The main advantage o portraits is that checks can still
be made once the person has passed through the access
barrier. I the access control gates are unmanned, then
sta members could easily allow strangers to enter
limited-access zones. But thanks to portraits, which can
be acquired without any special cooperation, it is possible
to check permanently that the people in protected zones
are indeed entitled to be there. Facial recognition can
thereore be used to extend access control by checking
presence in particularly sensitive environments.
Applications for the general public
Access to computerized servicesBiometric acial logins are already possible on certain
computers. But the system has come in or some
criticism, since logins are possible i a photo o the
user is shown instead o the user’s actual ace. Recent
algorithms are capable o detecting whether the ace
is indeed three-dimensional and mobile, and uturegenerations o biometric acial login systems will not be
ooled by photos.
Photo album managementFacial recognition applications are now available to
manage personal collections o photographs by
showing the names o persons in photos, i they already
appear in older pictures in the collection. Products
include iPhoto rom Apple and Picasa rom Google.
While this application may appear trivial, it shows the
ull potential o acial recognition, whose limits are still
ar rom known.
9
Figure 8: acial recognition manages personal collections
o photographs.
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The Technology
This chapter describes the dierent steps in the acial recognition process and
describes the main technologies that are available today.
The different steps of the automaticfacial recognition process
Step 1: image acquisitionThis step is decisive, because the precision o acial
recognition hinges on the quality o the images
acquired.
In this step, automatic acial recognition systems may
assess the quality o the acquired images. In interactive
acquisition, the portrait can be re-acquired in order to
obtain a better image that meets the criteria o the
image assessment process.
Step 2: ace localization, scaling andalignmentBeore comparing aces, it is rst necessary to nd
them in images that may contain all sorts o other
inormation and adjust them to the same scale, with the
head positioned vertically. This step is quite simple when
working on controlled portraits, because each image onlycontains one ace. But it is much more dicult to extract
a multitude o aces rom a video taken outdoors.
Step 3: enhancement o ace imagesOnce the aces have been ound and calibrated, they
need to be enhanced. By way o example, the eects
o compression can be minimized, inconsistent lighting
can be corrected or unusable zones (masked by a veil,or example) can be detected and excluded. In this step,
models can be applied to correct the orientation o
the ace, the eects o ageing and expressions. While
some enhancements can be made automatically, the
assistance o an operator may prove to be very useul
when working on dicult images.
Step 4: extraction o characteristicsMost acial recognition algorithms use mathematical
transormations in order to compare images. These
transormations can highlight the distinctive specic
eatures o an image: requencies, directions, contours,
etc. Transormed images can not usually be used by the
operator’s naked eye.
Step 5: representation as a template*and comparisonA binary record, or template, is extracted rom the
transormed image. The comparator then compares
this template with those o the images in the reerence
database and scores each image. The higher the score,
the higher the similarity with the image o the wanted
ace.
Figure 10: portrait comparison.
Figure 9: Portrait
acquisition.
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Step 6: thresholding and decision-makingI the search is simple, i.e. when the quality o the query
image and the images in the reerence database are
good, the operator should only receive a small number o
images that stand a high chance o matching the wanted
person. The ideal case is when the operator only receives
a candidate list when there’s a “hit” in the database, and
nothing otherwise, thus preserving the operator’s resources
and attention on relevant cases. This operation is calledthresholding. It requires a similarity unction that makes a
clear distinction between ”hits” and alse alarms.
The operator can then make a decision and make
changes to the system’s reerence database* according
to the requirements o the job in hand.
The facial recognition algorithms
This chapter contains an overview o the best known
algorithms. For more details, visit http://www.ace-rec.org/
algorithms/, which is an excellent source o inormation.
There are two prominent categories o algorithms when it
comes to acial recognition: procedural algorithms, which
imitate the analysis made by an operator, and machine
learning algorithms, which apply a mathematical logic
in order to dene and use the criteria that an operator
may not be capable o interpreting. Both categories o
algorithms can be used or dierent types o ace data:
xed images, videos, or 3D acquisitions.
Procedural algorithmsThe main procedural algorithms use the visible acial
landmarks, such as the corner o the eye, the middle othe upper lip, the lowest point o the chin or the details
and color o the skin.
Ater detecting the landmarks o the ace - a process
that may be manually assisted - the procedural
algorithms attempt to measure the coherence between
the parts o the two aces. They do this by using models
designed to demonstrate how a ace is distorted by its
expression, age, orientation and lighting.
The most commonly used algorithms in this category
are Elastic Bunch Graph Matching (EBGM) and the
comparison o acial texture. Facial texture analysis is
used in particular to distinguish twins.
These algorithms are used (at step 5 above) to convert
images into templates in order to compare them.
11
Figure 11: bunch graph matching.
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Training algorithmsTraining methods rely on an abstract process in order
to nd independently an optimal organization based on
examples. There is a great number o training algorithms.
Examples include:
• Vector projections. The input used by these methods
is a large vector - the characteristics extracted in
step 4 - which is then projected in a smaller space. Ithe two original images match the same person, they
must have close projected vectors. I they represent
two dierent persons, then the projected vectors are
more distant. It is the denition o the projection that
is complex, and leads to the training process. The most
common methods are:
Principal component analysis (PCA), which extracts
the most distinctive vectors rom a space. Eigenaces
apply this principle.
Linear discriminant analysis (LDA), which separates
dierent objects.
Independent component analysis (ICA), which keeps
the axes as independent rom one another as possible.
Non-linear methods, including Kernels and SVMs*
(Support Vector Machines).
• Neural networks. These networks use a set o cells
that transorm the inormation that they exchange
with one another. They resemble neurons, synapses
and nervous infux. Neural networks are dened by
training. In the comparison phase, the characteristics
extracted rom the portrait are entered or input into
the network. The network output is used to decide
whether the ace resembles the dierent aces in the
reerence database.
• Statistical methods. These methods seek to measure
the probability that a photo matches a statistical
model o the ace. Each ace is represented in the
comparator by its statistical model, e.g. by a number o
states, their respective probability and the probability
o transition rom one state to another. New acial
images are represented as a sequence o successive
Y
X
Y
Detection boundary
0.0496
0.0498
0.0494
0.0492
0.0490
0.0488
0.0486
0.0484
0.0482
1.058 1.060 1.062 1.064 1.066 1;068 1.070 1.072 1.07
= type A image
= type B image
Separation may be easier in higher dimensions
complex in low dimensions simple in higher dimensions
separating
hyperplane
map
Feature
Original image
25 x 25
I1
H1
H2
H50
O1
DB
O2
O40
I2
I3
I625
12
Figure 12: Axes created with principle component analysis.
Figure 13: Boundary detection with linear discriminant analysis.
Figure 14: Space transormation.
Figure 15: Sample neural network.
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states. For each ace in the reerence database, the
level o probability o the sequence can be determined
in order to decide whether the resemblance between
the two images is high or not.
Training algorithms are obviously used in step 5
(representation as templates and comparison), but they
can also be used to locate aces in images (step 2) and
to extract visible acial landmarks.
Special eatures o video processingA person’s ace appears several times in a video. It can
be viewed rom one video rame* to the next. These
multiple views are useul or acial recognition purposes,
because they can be used to obtain more inormation
about the ace than a single view. A range o tracking
techniques has thereore been developed. The most
robust techniques use movement statistics models. At
each step, they generate probabilized detection and
tracking hypotheses, which are consolidated in order to
make a decision. The actual comparison process uses
a series o images o the same person that are sorted
according to the quality o the views and the dierent
positions that they represent. Using these dierent views
rather than a single view – even i its quality is superior –
will always improve the precision o the search.
Special eatures o 3DFaces are naturally three-dimensional. I a ace is to be
completely represented, then its shape and the color or
texture o every part o the ace must be known. Photos
only contain the color o part o the ace. Thereore
they only contain partial, or 2D, inormation. Traditional
ace comparison techniques rely on these incomplete
data. Using all the inormation o the ace can only
serve to improve the precision o acial recognition. The
European 3D Face project (http://www.3dace.org/) has
demonstrated how the association o 2D data (texture)
and 3D data (shape) improves precision compared with
the use o only the texture or the shape.
The acquisition o three-dimensional images requires
special sensors that are not yet widely available. These
sensors work by projecting structured light onto the
ace or by stereoscopy*. Morpho has developed an
innovative 3D acquisition concept based on stereoscopy
that is capable o acquiring a ace on the fy when a
person passes through a control gate without stopping.
It is called Face on the Fly* technology.
3D technology can be used, even without any 3D
sensors. 3D morphable models can be used to take one
or more images o the same ace and associate a 3Dshape that matches the ace as closely as possible. This
association signicantly improves the robustness o the
comparison o oriented aces.
Light sourcesThe images used or acial recognition are usually taken
in visible light. But inrared images can also be used and
research is currently looking into other data types, such
as terahertz*.
Figure 16:
examples o pose
angles.
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Perormance… and comparison with otherorms o biometrics
Two gures are required to measure the perormance o a biometric technique:
• the false acceptance rate (FAR*), which measures the percentage of fraudsters
who are mistakenly accepted,
• the false rejection rate (FRR*), which measures the percentage of persons who are
not accepted, whereas they are not raudsters.
Ideally, the alse acceptance and rejection rates should
be zero. But in reality, biometric systems can be
characterized by the graphs on the model below.
The lower the rate o alse acceptances, the more
secure is the application. The lower the rate o alse
rejections, the greater the comort or users and the
more limited the work done by operators.
The tests in NIST’s FRVT 2006 demonstrated that,
with rontal portraits taken in a strictly controlled
environment, with high resolution and only slight
dierences in age, acial recognition is a very precise
biometric technique. False rejections totaled just 1%,
with 0.1% o alse acceptances. This means that, or
example, in the ideal passport control application, out
o 1,000 authentic passport holders, only 10 would
not get through the automatic check and would have
to call on an operator, while out o 1,000 raudsters
carrying passports that do not belong to them, 999
would be detected by the automatic control system.
But we all know that the ideal conditions implemented
by the NIST or FRVT 2006 are dicult to achieve
operationally. In addition to orientation, resolution
and lighting, acial recognition is also conronted with
problems due to physical changes and changes in
appearance: expression, changes o hair, changes in
weight, spectacles, hats, ageing, injuries, illness, etc.
The current acial recognition algorithms can only
tolerate limited variations in the portrait. For example,
i a person allows his beard to grow, then the automatic
portrait recognition system will recognize him with
almost the same reliability, as i his beard had not
changed. I the same person allows his beard to grow
and pulls a ace, the probability that the system will
recognize him drops a little, but stil l remains high. But
i a number o small changes are accumulated (by way
o example, i the person allows his beard to grow,
pulls a ace, does not ace the camera, remains a long
way away rom the camera and conceals a large part
o one o the sides o his ace with his hand), then the
Very low false rejections:comfort for users, weak controls
Very low falseacceptance: security
FALSE ACCEPTANCES
FALSE REJECTIONS
Figure 17: False acceptance – alse rejection graph.
Figure 18: Variations with age and orientation. Figure 19: Variations with accessories.
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probability that he wil l be recognized is much lower.
Since there are no test samples that can be used to
measure the impact, with the appropriate statistical
validity, o the dierent criteria that minimize the
precision o acial recognition, and since these criteria
are not independent, the drop in the precision o acial
recognition is not predictable in absolute terms.
Consequently, Morpho advises every potential userto proceed with tests and measurements o their own
data, depending on their own operational needs. But
these tests must not simply take account o the purely
algorithmic precision. They must also take the speed o
execution o the algorithms into consideration. A very slow
system will necessarily nd ewer hits than a ast system,
with the same power and precision, and will be more
dicult to adapt to the working procedures o the operators.
I one compares acial recognition with ngerprinting
and iris recognition, then it becomes clear that acial
recognition is intrinsically “trickier” than other biometric
techniques.
Facial recognition already works well and still has plenty
o potential or improvement. Nevertheless, it is quite
improbable that it will achieve the same levels o precision
as iris or ngerprint recognition in the short term.
Iris Fingerprints Face
Uniqueness Every iris is unique Every fngerprint is unique Two persons may
resemble one
another very closely
Number of images Two irises per person 10 fngerprints per person, One ace per person
to be acquired plus other parts o the body
where riction ridges are located
Stability over time Invariable rom birth Invariable rom childhood Changes with age,
state o health, etc.
Representation in Easy to represent
the dimensions in 2D and ew problems Easy to represent in 2D Intrinsically 3D
due to orientation
Distortions Pupil dilation Distortion limited by the elasticity Highly variable,
o the skin according to expressions
Resolution Requires high resolution Usually standard 500 dpi Acquired at any scale
Maturity of the processes Underdeveloped expertise Strong command o the Underdeveloped expertise
associated with identifcation process
the technology by generations o
fngerprinting experts
Comparison of biometric methods
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Implementing a acial recognition system
The deployment o a acial recognition system must take both technical and human
actors into consideration. Some o these actors, which are specic to acial
recognition, are quite important.
Integrating facial recognition inthe existing technical environment
Photos are a very common orm o data. Most potentialusers o acial recognition thereore already possess a
number o inormation systems that can be interaced
with a acial recognition system:
• databases o individuals (with portraits) or criminal cases
• other systems designed to compare biometric data
• biometric data acquisition systems
There is almost always a business interest to be gained
by integrating a acial recognition system with the
existing environment.
Integration with databases allows or a simplied recovery
o existing data and allows legacy data acquisition
processes to be used. By way o example, in order to
add a acial recognition unction to a civil status register
in order to detect identity raud, it is always preerable
to keep the same civil status register and operate the
acial recognition system in back oce mode, without
impacting the potentially complex processes that are
used with the civil status register.
Integration with other biometric systems provides
or both optimized, redundancy-ree data acquisition
processes and the benets o using a number o
methods that increase the perormance o searches and
require operators to veriy only the most dicult cases.
Even i biometric searches are not natively consolidated,multi-biometrics does increase perormance:
• it allows cases to be processed, when one modality is
absent or is o poor quality,
• it can make more extensive, and consequently more
ecient links. For example, in a criminal police system,
i it is known that a rst oense o bank card raud
and a second oense o shopliting were committed
by the same person, because the images show the
same ace, and i the bank card raud is solved using
ngerprints, then it is highly likely that the shopliting
oense will also be solved. Two isolated systems would
not come to the same conclusion.
In an eort to acilitate the integration o acial
recognition with other inormation systems, Morpho has
developed generic interaces that meet the ANSI/NIST-
ITL 1-2007 standard or the data ormat or ngerprints,
aces and other biometric data.
Managing the expectationsof customers and operators
Facial recognition seems to be so simple and intuitive
that the expectations relative to this technique may be
out o all proportion. The unsuccessul experiment in
Tampa, Florida, USA in 2003 is one notable example.
A acial recognition system was deployed in order
to recognize wanted persons in a crowd. But the
operators only received alse alerts. Preliminary tests in
a controlled environment and an elementary probability
calculation could have concluded that the technology
o the time was not suited to acial recognition
in crowds.
Thereore, it is advisable that every potential user o
acial recognition conducts tests in order to assess
the suitability o the technology to their operational
Figure 20:
identication
verication
screen within
the verication
application.
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17
applications beore proceeding with deployment. The
independent tests conducted in 2006 by the BKA (1)
and also the NPIA(2) serve as an example. Research
reports explain perectly the state o the art, the tests
conducted, the conclusions and the outlook.
In order to conduct these tests, Morpho has developeda very simple pilot system containing the most recent
advances in algorithms that can be installed and
programmed in less than one day. In this way, it is
possible to test acial recognition usage scenarios,
check the results that may be obtained or a given
target application and measure the workload required
to obtain these results. Morpho can provide support to
its potential customers in this assessment process.
Developing the expertise o operatorsAcquiring acial images and recognizing people on
photos is not easy and requires the development o
specic expertise. Morpho can provide training in its
products.
Expertise in data acquisitionI acial recognition is to be ecient, then the quality o
the images in the reerence database must be satisactory.
The criteria that measure this quality are dened by the
ISO* standard, and the automatic acquisition systems
are capable o veriying most o these criteria. But the
judgment o an operator remains the best guarantee o
good image acquisition. Automatic checks cannot be
100% reliable and the subject that is lmed may have
particular physical properties that prevent certain criteria
rom being reached.
Similarly, the operator is the person who is best placed
to judge the suitability o an image or acial recognition
searches. When acquiring an image in the eld or an
identity check, lming people behaving violently in a
demonstration or choosing which images rom many
images o the same ace should be used or search
purposes, the operator’s expertise is decisive in making
optimal use o acial recognition.
Expertise in checking searchesVisually recognizing people may appear to be simple.
When working on subjects that one knows well, and
that are seen ace to ace rather than on a photo, even
little children are capable o recognizing a ace. But
even known persons can be misidentied i they appear
on blurred or old photos, or i they have an unusualexpression. And when the person is unknown, the quality
o the photos is variable and the angles and lighting
dier, visual recognition becomes a tricky task, resulting
in many errors detected by academic studies.
A number o methods have been developed to improve
the visual recognition o persons, but they are not
as robust as the methods used to check ngerprints.
The relevance o recognition criteria (e.g. the stability
o wrinkles) remains to be scientically established.
Thereore, in order to avoid making mistakes and wasting
time, it is necessary to develop methods and training or
operators in the use o the acial recognition system. It is
also important to cooperate with academic researchers
and acial recognition technology vendors in order to
make progress in terms o both the practices and the
tools in this eld.
Helping the end usersEnd users react very dierently to biometric systems.
Reactions range rom total hostility (against a society
that some people eel is obsessed with security) to
a certain amusement at being a pioneer in the use o
new technology. Whether they be cooperative or hostile,
they are all novices, and it is essential to give them clear
and concise instructions on the behavior to be adopted.
In applications or the general public, such as passport
controls, it is impossible to support the users one by one.
This is the reason why close attention must be paid to the
ease o use o biometric tools or end users; they must be
as enjoyable to operate as possible.
The SmartGate* passport control system deployed in
Australia meets this need. The end users are happy with
the system and preer automatic passport controls using
acial recognition to conventional control gates.
(1) BKA, Bundeskriminalamt, the German police authorities.
(2) NPIA: National Police Improvement Agency. An organization tasked with making technological recommendations to the British police orce
in order to improve eciency.
Figure 21: on-the-fy portrait acquisition or traveller screening
in airports.
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The market associated with automaticacial recognitionThe market for facial recognition
Compared with other biometric techniques
(ngerprinting, iris recognition, etc.), acial recognition
accounts or 16% o the market (source: Frost & Sullivan,
2007, 2008).
Also according to Frost & Sullivan, the value o this
market is growing constantly, with an average annual
growth rate o 54%. Totaling €72.7 million in 2006
and about €250 million in 2009, the acial recognition
market should exceed €622 million by 2011 and
€1 billion in 2013. These estimates correspond to all the
links in the value chain o developments in the eld o
acial recognition.
Driving forces and obstacles
A number o dierent actors drive or hinder the
development o acial recognition, ranging rom
technology to politics, applications and even standards.
The driving orces• The creation o an international standard (ICAO) or
travel documents. This standard species three possible
orms o biometrics: iris recognition, ngerprinting
and acial recognition. It has resulted in the creation
o a reerence database o high quality portraits.
Components /
Imaging sensors
1- 2D/3D cameras
2- Photo-video
Development of high
accuracy face recognition
algorithms,
in 2D and 3D, for static
images and video
Development and
integration of FR
utilization in vertical
markets
Deployement or upgrade
of integrated FR oriented
solutions in public
security market
Face Tech.
DevelopmentSW Development
System
Integration
Facial Recognition: value chain
16% 84%
18
Figure 22: Face recognition share in the biometric market.
Figure 23: Facial recognition: value chain.
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• The highly avorable conclusions o FRVT 2006.
Facial recognition o good quality images is a mature
biometric technique and the algorithms continue to
progress, opening the way or the combination o 2D
and 3D acial recognition techniques.
• Existing implementations. Notably, governmental
or inter-governmental identity programs (electronicpassports, ID cards, driving licenses, etc.) and
automatic border passport control programs. These
implementations have been a real success and the
expectations o potential users are becoming more
precise and realistic.
• The proven benets o multi-biometrics. Multi-
biometrics cut stang costs in biometric research,
can solve dicult cases in which the data rom one
o the biometric techniques is o poor quality and can
correlate the connections made by dierent biometric
techniques.
• The availability o video cameras with improved
resolution. These cameras take better pictures, which
allow or more precise acial recognition.
The obstacles• Facial recognition is intrinsically more dicult than
other major biometric techniques.
• Facial recognition is oten quoted as an inringement
o civil liberties.
• Applications are broader than or other biometric
techniques and the potential new users must be
introduced to the eld o biometrics and understand
its benets.
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The experience o Morphoin the feld o acial recognition
Morpho has been active in the eld o acial recognition since 2002. Some o the
most signicant milestones in our activity include:
• 2002 assessment o the algorithms on the market: we
opted to start with technology rom Cognitec, whichwas ound to be the best on the market at the time
by the NIST’s FRVT 2002. Since then, Morpho has
signicantly improved the technology.
• Morpho deployed a pilot tool designed to assess the
technology with simple and ecient user interaces.
The tool allowed numerous police agencies to test the
possible applications o acial recognition. This pilot
allowed a number o conclusions to be drawn:
The Morpho subsidiary MorphoTrak received the “Best
Biometric Identication Technology” award or its acial
recognition pilot system when it was exhibited at the
Global Border Security Conerence and Expo in Austin,
Texas in May 2008.
The Australian police orce concluded that acial
recognition oered decisive benets or the correlation
o crim inal ca ses and now use s Morph o technology
in operational applications.
The University o Lausanne, in cooperation with
the Romande regional police orce in Switzerland,successully developed a strategy or the use o Morpho’s
acial recognition or demonstrations.
The Pierce County Sheri’s Oce in Washington, USA,
demonstrated how Morpho’s acial recognition reached
a level o precision higher than 94% when identiying
individuals in its collections, even with signicant
dierences in age. It also surmised that the combined
use o Morpho’s acial recognition and ngerprinting
technology could allow subjects to be identied without
calling on ngerprinting experts.
In 2008, the Mexican police conrmed that Morpho’s
technology was the astest and the most accurate on
the market in operational tests.
In 2009, the Paris police authorities helped
investigators by piloting a Morpho acial
recognition system on a reerence database o
470,000 aces.
• Morpho developed the SmartGate border control
system that is now used in all international airports in
Australia and will soon be deployed in New Zealand.
This project uses the photos on biometric passports
to speed up and acilitate passport controls. By the
end o 2008, 150,000 people had passed through a
SmartGate.
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Websites:http://face.nist.gov/mbgc/mbgc_presentations.htm
http://www.hcfdc.org/trophees2009/palmares.php
http://www.3dface.org/
NB : On May 27 2010, Sagem Sécurité changed its name to Morpho. For events, benchmarks having taken place before
this date, we are listed or quoted under the name of Sagem Sécurité.
• Morpho also developed MorphoFace™ Investigate*
on the basis o recommendations made by our users
working in dedicated Focus Groups. This product is
designed to perorm identication tasks, to help the
police to solve criminal cases or simply or identication
purposes in civil applications.
• Morpho’s research laboratories continuously develop
and improve the basic technology. Research ocuses on
acial recognition algorithms, but also the optimization
o portrait acquisition and the extraction o portraits
rom surveillance video ootage. As a result o this
research work, Morpho nished rst in the NIST’s
portal challenge in March 2009. In May 2009, Morpho
received the HCFDC (French High Committee or Civil
Deense) innovation trophy or its Face on the fy ace
acquisition technology.
Morpho takes part in cooperative research projects in the
United States and Europe, including the 3D Face project,
which has concluded that the use o both 2D and 3D
acial data improves the precision o acial recognition
or travel document applications (http://www.3dace.
org/).
Morpho is making signicant investments in innovation
and the development o its acial recognition technology
in order to consolidate its position as leader in biometrics.
Major advances are continuously being made in terms
o both the quality and diversity o the algorithms and
the development o dedicated products adapted to the
needs o the market.
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Glossary and acronyms
AAlert - An alert is an automatic and
interactive event that is generated when
analyzing video streams. Alerts are generated
when the recognition algorithms conclude
that a person seen in the video is very
probably contained in the reerence
database. Alerts appear in the userinterace by displaying the image o the
person spotted in the video next to the
image in the reerence database.
FFA - False Acceptance. False acceptances
and alse rejections are used to measure
the perormance o a biometric system.
False acceptances correspond to raudsters
that the biometric control ails to detect.
Face on the Fly - Face On The Fly is an
innovative technology developed by
Morpho. The purpose o this
technology is to acquire acial images when
a person passes through a control gate,
without stopping and without having
to look at a particular camera. Several
images are acquired by a series o cameras
to create a three dimensional view o the
ace. A rontal projection o this image can
then be compared and used or
authentication or identifcation purposes.
Face On The Fly technology won the French
High Committee or Civil Deense’s award
or technological innovation in May 2009.
FR - False Rejection. False rejections
correspond to authorized users that the
biometric control ails to recognize.
Frame - An image extracted rom a video
recording or stream.
FRVT - Face Recognition Vendor Test.
I ICAO - International Civil Aviation
Or g a n i za t i o n . An i n t e r n a t i o n a l
organization that is part o the United
Nations. Its mission is to contribute to
the development o standards used to
standardize international air transport
ISO - International Organization orStandardization. An international
organization made up o the national
standardization institutes rom more
than 100 countries.
LLine-up - Line-up techniques are used by
the police to determine whether a witness
has spotted a suspect. The suspect is
presented to the witness amongst a group
o physically similar people. Witnesses
must then decide whether they recognize
one o the members o the group.
MMBGC - Multi Biometric Grand Challenge
(http://ace.nist.gov/mbgc/).
A test organized by the NIST to make
advances in research into the recognition
o persons rom a distance using acial
recognition and iris recognition.
MFI - MorphoFace™ Investigate.
A acial recognition system developed
by Morpho. This system is essentially
designed or use in police investigations.
It can be used to solve cases rom portrait
traces let on the scene o the crime.
Modality - A type o biometrics.
NNIST - National Institute o Standards
a n d T e ch n o l o g y . T h e Am e r i ca n
standardization organization and a
member o ISO.
R
Reference database - A database opersons o interest to be identied in the
images and videos to be processed.
SSmartGate - SmartGate is a project
run by Australian customs. The purpose
o the project is to speed up customs
clearance in Australia’s international
airports using the portraits on electronic
passports and acial recognition. Morpho
deploys a system in Australian airports as
part o this project.
Stereoscopy - Stereoscopy reers to
all the techniques used to reproduce a
perception o a contour rom several fat
images.
SVM - Support Vector Machine. A
prominent technique used to solve
classication problems.
TTemplate - The code extracted rom an
image o a ace by image processing.
Facial comparison is carried out via
extracted templates.
Terahertz - An electromagnetic wave in
the electromagnetic spectrum between
inrared (the optical domain) and
microwaves (the electronic domain).
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Bibliography
ForshungsProjekt, Gesichtserkennung als Fahndungshilsmittel,
Fo t o - Fa h n d u n g , Ab s ch l u s sb e r i ch t , B K A , 2 0 0 7 ,
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de/kriminalwissenschaten/otoahndung/aq.html
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“CCTV on trials” , Josh P Davis and Tim Valentine, Goldsmiths,
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“Morphological Classifcation o Facial Features in Adult Caucasian
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o Glasgow, Glasgow, UK, Justice O The Peace Volume 172,
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Universitatsklinikum, Ulm, Germany.
“Failure o Anthropometry as a Facial Identifcation Technique
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America A 4: 519–524, 1987.
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Springer, 2005, ISBN 0387260501, 9780387260501, page 146.
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Phone: +33 (0) 58 11 88 76 - Fax: +33 (0) 58 11 87 81
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Société anonyme au capital de 159.876.075 e 440 305 282 RCS PARIS