Seminar on Face Recognigion System

8/6/2019 Seminar on Face Recognigion System

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Computer Science Department FACE RECOGNITION

Face Recognition Technology

A SEMINAR REPORT

Submitted By

XXXXXX XXXXXX

In partial fulfillment for the award of the degree

of

BACHELOR OF TECHNOLOGYIN

COMPUTER SCIENCE & ENGINEERING

AT

College logo

Yagyavalkya Institute of

Technology

JAIPUR

SESSION 2009-2010


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Preface

Engineering is associated with skill, creativity and judgment,

which is not a theory subject but an art which can be obtained

with systematic study, observation and practice. To bridge the

gap of theoretical and practical study and to provide a solution for

the same practical knowledge is indispensable. In the college

circulation we usually get the theoretical knowledge of industries,

as how it works. But how can we prove our theoretical knowledge

to increase the productivity or efficiency of the industry?

To overcome the same problem, we, the students ofXXXXXXXXXXXX Institute of Technologyare supposed to goa practical training of 30 days at the end of sixth semester as the

time is predefined to be familiar with industrial environment.

This report briefly describes a study-cum-report on Face

Recognition Technology and other related features.

Without such practical works, only theoretical Engineering is of

little value. Surely, it will be highly beneficial for us.

XXXXX XXXXX

FINAL

B. [COMPUTER SCIENCE]


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Acknowledgment

I sincerely acknowledge Mr. XXXXXX XXXXX Head, Computer

engineering Department, YIT, Jaipur who helped us a lot so that

project was completed within specified period. We are extremely

grateful to him for his kind consent, co-operation and

encouragement. We are greatly motivated by his character of

looking everything from completely differently angle and his

never ending enthusiasm work. I also learned a lot from whileattending his sessions.

We acknowledge Mr. XXXXXX XXXXX (Seminar Coordinator),

who has helped and guided us throughout in the completion of

this project as well as the challenges that lie behind us. He is

always there to meet and talk about my ideas, to proofread and

mark up my project. We are really fortunate to work under this

guidance .He was instrumental in making us understand how to

implement the project and also provided continuous supervision

of the project.

This project has been benefited from the many useful comments

provided to me by the numerous of my colleagous. In addition

many other of my friends have checked it and have offered many

suggestions and comments. Besides there are some books and

some online helps. Although I cannot mention all these people

here, I thank each and everyone who supported me on this.


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CERTIFICATE

Date:

This is to certify that the seminar titled Face Recognition

Technology submitted by XXXXX XXXX in partial

fulfillment for the award of degree of Bachelor of

Technology of Rajasthan Technical University, Kota has

been carried out under my supervision during the

academic year 2009-2010.This work has not been

submitted partially or wholly to any other University or

Institute for the award of this or any other degree or

diploma.

Signature

Signature

Mr. XXXX XXXXX


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Mr. XXXXX XXXXX

HOD CSSeminar Guide

Contents

Page no.

1.

Abstract

6

2.

Introduction.. 7

3. FRT

operation

8

4. FRT task..

.

.10

5. Step of facial

recognition..13


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6. Moral consideration of FRT..

.15

7.

Difficulties

18

8.

Conclusion

21

9.References

22


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ABSTRACT

A face recognition technology is used to automatically identify a person through adigital image. It is mainly used in security systems. The face recognition will

directly capture information about the shapes of faces. The main advantage of

facial recognition is it identifies each individuals skin tone of a human faces

surface, like the curves of the eye hole, nose, and chin, etc. this technology may

also be used in very dark condition. It can view the face in different angles to

identify.

It is mainly used in airports were it ill recognize the face and we can avoid some

unwanted terrorist. When compared with other biometrics systems usingfingerprint and iris, face recognition has different advantages because it is without

touching the person. Trough Face images we can capture the person identification

from a distance without touching or interacting with them. And also face

recognition is used for crime restriction purpose because face images that have

been recorded and archived, so that it ill help us to identify a person later.

This report develops a socio-political analysis that bridges the technical and social-

scientific literatures on FRT and addresses the unique challenges and concerns that

attend its development, evaluation, and specific operational uses, contexts, and

goals. It highlights the potential and limitations of the technology, noting thosetasks for which it seems ready for deployment, those areas where performance

obstacles may be overcome by future technological developments or sound

operating procedures, and still other issues which appear intractable. Its concern

with efficacy extends to ethical considerations


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Introduction to FRT (how does it work?)

Facial recognition research and FRT is a subfield in a larger field of pattern

recognition research and technology. Pattern recognition technology uses

statistical techniques to detect and extract patterns from data in order to

match it with patterns stored in a database. The data upon which the

recognition system works (such as a photo of a face) is no more than a set of

discernable pixel-level patterns for the system, that is, the pattern

recognition system does not perceive meaningful faces as a human would

understand them.

Nevertheless, it is very important for these systems to be able to locate ordetect a face in a field of vision so that it is only the image pattern of the

face (and not the background noise) that is processed and analyzed. This

problem, as well as other issues, will be discussed as the report proceeds.

In these discussions we will attempt to develop the readers understanding

of the technology without going into too much technical detail. This

obviously means that our attempts to simplify some of the technical detailmight also come at the cost of some rigor. Thus, readers need to be careful to

bear this in mind when they draw conclusions about the technology.

Nevertheless, we do believe that our discussion will empower the

policymaker to ask the right questions and make sense of the

pronouncements that come from academic and commercial sources.


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FRT in operation

Overview

Figure 1 below depicts the typical way that a FRT can be used for

identification purposes. The first step in the facial recognition process is the

capturing of a face image, also known as the probe image. This would

normally be done using a still or video camera.

In principle, the capturing of the face image can be done with or without the

knowledge (or cooperation) of the subject. This is indeed one of the most

attractive features of FRT. As such, it could, in principle, be incorporated

into existing good quality passive CCTV systems. However, as we willshow below, locating a face in a stream of video data is not a trivial matter.

The effectiveness of the whole system is highly dependent on the quality7and characteristics of the captured face image. The process begins with face

detection and extraction from the larger image, which generally contains a

background and often more complex patterns and even other faces.

The system will, to the extent possible, normalize (or standardize) the

probe image so that it is in the same format (size, rotation, etc.) as the

images in the database. The normalized face image is then passed to therecognition software. This normally involves a number of steps such as

extracting the features to create a biometric template or mathematical

representation to be compared to those in the reference database (often

referred to as thegallery).

In an identification application, if there is a match, an alarm solicits an

operators attention to verify the match and initiate the appropriate actions.

The match may either be true, calling for whatever action is deemed

appropriate for the context, or it may be false (a false positive), meaningthe recognition algorithm made a mistake. The process we describe here is a

typical identification task.


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Figure 1: Overview of FRS

FRT can be used for a variety of tasks. Let us consider these in more detail.


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FRT tasks

FRT can typically be used for three different tasks, or combinations of tasks:

verification, identification, and watch list. Each of these represents distinctivechallenges to the implementation and use of FRT as well as other biometric

technologies.Home Safety and Security Systems

Because Z-Wave can transceive commands based on real time conditions, and is

able to control devices in intelligent groupings, it allows novel extensions of

traditional home security concepts. As an example, the opening of a Z-Wave

enabled door lock can de-activate a security system and turn on lights when

children arrive home from school, and send a notification to a parent's PC or cell

phone via the Internet. Opening a Z-Wave enabled garage door can trigger exterior

and interior home lights, while a Z-Wave motion detector can trigger an outdoor

security light and a webcam, which would allow the end user to monitor the home

while away.

Home Entertainment

Z-Wave's ability to command multiple devices as a unified event makes it well

suited for home audio and video applications. For example, a simple "Play DVD"

command on the remote control could turn on the needed components, set them to

the correct inputs and even lower motorized shades and dim the room lights. Z-

Wave's RF technology is also well suited as an evolution of conventional infrared

(IR) based remote controls for home electronics, as it is not constrained by IR's line

of sight and distance limitations. In January of 2008, Zensys announced a single-

chip solution that pairs Z-Wave with IR control, positioning the technology as an all

encompassing solution for home remote controls.

1.3 Setting up a Z-Wave network


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Z-Wave mesh networks can begin with a single controllable device and a controller.

Additional devices can be added at any time, as can multiple controllers, including

traditional hand-held controllers, key-fob controllers, wall-switch controllers and PC

applications designed for management and control of a Z-Wave network.

A device must be "included" to the Z-Wave network before it can be controlled via

Z-Wave. This process (also known as "pairing" and "adding") is usually achieved by

pressing a sequence of buttons on the controller and the device being added to the

network. This sequence only needs to be performed once, after which the device is

always recognized by the controller. Devices can be removed from the Z-Wave

network by a similar process of button strokes.

This inclusion process is repeated for each device in the system. Because the

controller is learning the signal strength between the devices during the inclusion

process, the devices themselves should be in their intended final location before

they are added to the system.

However, once a device has been introduced into a network, it can become

troublesome to remove the unit without actually having the functional unit present.

A number of Z-Wave users have complained that a Z-Wave controller can be

functionally destroyed by the bulb that it controls blowing and any controlling units

then report errors every time a command that would affect that unit is sent, i.e.,

group commands / scene commands / all-on / all-off, etc. The only way to restore

the service to a non-error reporting state is to factory reset all controllers and then

relearn all Z-Wave devices.

1.4 Z-Wave Alliance

The Z-Wave Alliance is a consortium of over 160 independent manufacturers who

have agreed to build wireless home control products based on the Z-Wave

standard. Principal members include Cooper Wiring Devices, Danfoss, Fakro,

Ingersoll-Rand, Intel, Intermatic, Leviton, Universal Electronics, Wayne-Dalton, Z-

Wave and Zensys.


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Products and applications from the Z-Wave Alliance fall into all major market

sectors for residential and light commercial control applications. These include

lighting, HVAC and security control, as well as home theaters, automated window

treatments, pool and spa controls, garage and access controls and more.

1.5 Radio specifications

Bandwidth: 9,600 bit/s or 40 kbit/s, fully interoperable

Modulation: GFSK

Range: Approximately 100 feet (or 30 meters) assuming "open air" conditions, with

reduced range indoors depending on building materials, etc.

Frequency band: The Z-Wave Radio uses the 900 MHz ISM band: 908.42MHz (United

States); 868.42MHz (Europe); 919.82MHz (Hong Kong); 921.42MHz (Australia/New

Zealand).

1.6 Radio specifics

In Europe, the 868 MHz band has a 1% duty cycle limitation, meaning that a Z-Wave unit canonly transmit 1% of the time. This limitation is not present in the U.S. 908 MHz band, but U.S.

legislation imposes a 1 mW transmission power limit, as opposed to 25 mW in Europe. Z-Wave

units can be in power-save mode and only be active 0.1% of the time, thus reducing powerconsumption dramatically.

1.7 Topology and routing

Z-Wave uses a Source-routed mesh network topology and has one or more master controllers

that control routing and security. Devices can communicate to another by using intermediatenodes to actively route around household obstacles or radio dead spots that might occur. A

message from node A to node C can be successfully delivered even if the two nodes are not

within range, providing that a third node B can communicate with nodes A and C. If the


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preferred route is unavailable, the message originator will attempt other routes until a path is

found to the "C" node. Therefore a Z-Wave network can span much further than the radio range

of a single unit, however with several of these hops a delay may be introduced between thecontrol command and the desired result. In order for Z-Wave units to be able to route unsolicited

messages, they cannot be in sleep mode. Therefore, most battery-operated devices are not

designed as repeater units. A Z-Wave network can consist of up to 232 devices with the option ofbridging networks if more devices are required.

2. What is

Z-Wave?

Home networking.

Home communication.

Home automation.

Security.

2.1 Wireless sensor network

A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to

cooperatively monitor physical or environmental conditions, such as temperature, sound,

vibration, pressure, motion or pollutants. The development of wireless sensor networks wasmotivated by military applications such as battlefield surveillance. They are now used in many

industrial and civilian application areas, including industrial process monitoring and control,

machine health monitoring, environment and habitat monitoring, healthcare applications, homeautomation, and traffic control.

In addition to one or more sensors, each node in a sensor network is typically equipped with a

radio transceiver or other wireless communications device, a small microcontroller, and an

energy source, usually a battery. A sensor node might vary in size from that of a shoebox downto the size of a grain of dust, although functioning "motes" of genuine microscopic dimensions

have yet to be created. The cost of sensor nodes is similarly variable, ranging from hundreds of

dollars to a few pennies, depending on the size of the sensor network and the complexityrequired of individual sensor nodes. Size and cost constraints on sensor nodes result in

corresponding constraints on resources such as energy, memory, computational speed and

bandwidth.

A sensor network normally constitutes a wireless ad-hoc network, meaning that each sensorsupports a multi-hop routing algorithm (several nodes may forward data packets to the base

station).


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2.2 HOME AUTOMATION

Home automation (also called domotics) designates an emerging practice of increased

automation of household appliances and features in residential dwellings, particularly throughelectronic means that allow for things impracticable, overly expensive or simply

not possible in recent past decades. The term may be used in contrast to the more mainstream

"building automation", which refers to industrial uses of similar technology, particularly the

automatic or semi-automatic control of lighting, doors and windows, Heating, Ventilation andAir Conditioning, and security and surveillance systems.

The techniques employed in home automation include those in building automation as well as

the control of home entertainment systems, houseplant watering, pet feeding, changing the

ambiance "scenes" for different events (such as dinners or parties), and the use of domesticrobots.

Typically, it is easier to more fully outfit a house during construction due to the accessibility ofthe walls, outlets, and storage rooms, and the ability to make design changes specifically to

accommodate certain technologies. Wireless systems are commonly installed when outfitting apre-existing house, as they obviate the need to make major structural changes. These

communicate via radio or infrared signals with a central controller.

Overview and Benefits

As the amount of controllable fittings and domestic appliances in the home rises, the ability ofthese devices to interconnect and communicate with each other digitally becomes a useful and

desirable feature. The consolidation of control or monitoring signals from appliances, fittings or

basic services is an aim of Home automation.

In simple installations this may be as straightforward as turning on the lights when a personenters the room. In advanced installations, rooms can sense not only the presence of a person

inside but know who that person is and perhaps set appropriate lighting, temperature, music

levels or television channels, taking into account the day of the week, the time of day, and otherfactors.

Other automated tasks may include setting the air conditioning to an energy saving setting when

the house is unoccupied, and restoring the normal setting when an occupant is about to return.

More sophisticated systems can maintain an inventory of products, recording their usage through

an RFID tag, and prepare a shopping list or even automatically order replacements.

Home automation can also provide a remote interface to home appliances or the automation

system itself, via telephone line, wireless transmission or the internet, to provide control and

monitoring via a Smart Phone or Web browser

An example of a remote monitoring implementation of home automation could be when a smoke

detector detects a fire or smoke condition, then all lights in the house will blink to alert any


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occupants of the house to the possible fire. If the house is equipped with a home theatre, a home

automation system can shut down all audio and video components to display the alert or make an

audible announcement. The system could also call the home owner on their mobile phone to alertthem, or call the fire brigade or alarm monitoring company to bring it to their attention.

Standards and bridges

There have been many attempts to standardize the forms of hardware, electronic and

communication interfaces needed to construct a home automation system. Specific domesticwiring and communication standards include:

BACnet

INSTEON

X10

KNX (standard)

LonWorks

C-Bus

SCS BUS with OpenWebNet

Universal powerline bus (UPB)

ZigBee and,

Z-Wave

Some standards use additional communication and control wiring, some embed signals in theexisting power circuit of the house, some use radio frequency (RF) signals, and some use a

combination of several methods. Control wiring is hardest to retrofit into an existing house.

Some appliances include USB that is used to control it and connect it to a domotics network.

Bridges translate information from one standard to another (eg. from

The New Standard in Wireless Remote Control

Z-Wave powers Z~Series. And to do so it takes the principals of modern, wireless technology

and applies it to home automation and control. To do so it uses all the principals that are

important to modern home automation: reliability, affordability, and the ability to install withoutlaying down cables or knocking holes in walls.


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Z-Wave makes any home a smart home quickly, easily and affordably!

Z-Wave is a next-generation wireless ecosystem that lets all your homeelectronics talk to each other, and to you, via remote control. It uses simple,reliable, low-power radio waves that easily travel through walls, floors and

cabinets. Z-Wave control can be added

Verification (Am I the identity I claim to be?)

Verification or authentication is the simplest task for a FRT. An individual

with a pre-existing relationship with an institution (and therefore already

enrolled in the reference database or gallery) presents his or her biometric

characteristics (face or probe image) to the system, claiming to be in the

reference database or gallery (i.e. claiming to be a legitimate identity).

The system must then attempt to match the probe image with the particular,claimed template in the reference database. This is a one-to-one matchingtask since the system does not need to check every record in the database but

only that which corresponds to the claimed identity (using some form of

identifier such as an employee number to access the record in the reference

database).

There are two possible outcomes: (1) the person is not recognized or (2) the

person is recognized. If the person is not recognized (i.e., the identity is not

verified) it might be because the person is an imposter (i.e., is making an

illegitimate identity claim) or because the system made a mistake (thismistake is referred to as afalse reject). The system may also make a mistake

in accepting a claim when it is in fact false (this is referred to as a false

accept).

The relationship between these different outcomes in the verification task is

indicated in Figure 2


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Figure 2: Possible outcomes in the verification task

Identification (Who am I or What is my identity?)

Identification is a more complex task than verification. In this case, the FRT

is provided a probe image to attempt to match it with a biometric reference

in the gallery (or not). This represents a one-to-manyproblem.

In addition, we need to further differentiate between closed-set identification

problems and open-set identification problems. In a closed-setidentification

problem we want to identify a person that we know is in the referencedatabase or gallery (in other words for any possible identification we want to

make we know beforehand that the person to be identified is in the

database).

Open-setidentification is more complex in that we do not know in advancewhether the person to be identified is or is not in the reference database. The

outcome of these two identification problems will be interpreted differently.

If there is no match in the closed-set identification then we know the system

has made a mistake (i.e., identification has failed (a false negative)).


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However in the open-set problem we do not know whether the system made

a mistake or whether the identity is simply not in the reference database in

the first instance. Real-world identification applications tend to be open-set

identification problems rather than closed-set identification problems.

Figure 3: Possible outcomes in the identification task


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Five Steps to Facial Recognition

1.Capture image

2. Find face in image

3. Extract features

4. Compare templates

5. Declare matches

As a biometric, facial recognition is a form of computer vision that usesfaces to attempt to identify a person or verify a persons claimed identity.

Regardless of specific method used, facial recognition is accomplished in a

five step process.

1. First, an image of the face is acquired. This acquisition can be accomplished by

digitally scanning an existing photograph or by using an electro-optical camera to

acquire a live picture of a subject. As video is a rapid sequence of individual still

images, it can also be used as a source of facial images.

2. Second, software is employed to detect the location of any face in the acquired

image. This task is difficult, and often generalized patterns of what a face looks

like (two eyes and a mouth set in an oval shape) are employed to pick out the

faces.

3. Once the facial detection software has targeted a face, it can be analyzed. As

noted in slide three, facial recognition analyzes the spatial geometry of

distinguishing features of the face. Different vendors use different methods to

extract the identifying features of a face.


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Thus, specific details on the methods are proprietary. The most popular method is

called Principle Components Analysis (PCA), which is commonly referred to as

the eigen face method. PCA has also been combined with neural networks and

local feature analysis in efforts to enhance its performance.

Template generation is the result of the feature extraction process. A template is a

reduced set of data that represents the unique features of an enrollees face. It is

important to note that because the systems use spatial geometry of distinguishing

facial features, they do not use hairstyle, facial hair, or other similar factors.

4. The fourth step is to compare the template generated in step three with those ina database of known faces. In an identification application, this process yields

scores that indicate how closely the generated template matches each of those in

the database. In a verification application, the generated template is only compared

with one template in the database that of the claimed identity.

5. The final step is determining whether any scores produced in step four are high

enough to declare a match. The rules governing the declaration of a match areoften configurable by the end user, so that he or she can determine how the facial

recognition system should behave based on security and operational

considerations.


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Moral considerations of FRT

This report has considered technical merits of FRT and FRS, particularly as they

function in real-world settings in relation to specific goals. Although certain

barriers to performance might be overcome by technical breakthroughs or

mitigated by policies and guidelines, there remains a class of issues deserving

attention not centered on functional efficiency but on moral and political concerns.

These concerns may be grouped under general headings of privacy, fairness,

freedom and autonomy, and security. While some of these are characteristically

connected to facial recognition and other biometric and surveillance systems,

generally, others are exacerbated, or mitigated, by details of the context,installation, and deployment policies. Therefore, the brief discussion that follows

not only draws these general connections, it suggests questions that need

addressing in order to anticipate and minimize impacts that are morally and

politically problematic.

Privacy is one of the most prominent concerns raised by critics of FRS. This is

not surprising because, at root, FRS disrupts the flow of information by connectingfacial images with identity, in turn connecting this with whatever other information

is held in a systems database. Although this need not in itself be morally

problematic, it is important to ascertain, for any given installation, whether these

new connections constitute morally unacceptable disruptions of entrenched flows

(often regarded as violations of privacy) or whether they can be justified by the

needs of the surrounding context. We recommend that an investigation into

potential threats to privacy be guided by the following questions:

Are subjects aware that their images have been obtained for and included

in the gallery database? Have they consented? In what form?

Have policies on access to the gallery been thoughtfully determined and

explicitly stated?


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Are people aware that their images are being captured for identification

purposes? Have and how have they consented?

Have policies on access to all information captured and generated by the

system been thoughtfully determined and explicitly stated?

Does the deployment of an FRS in a particular context violate reasonable

expectations of subjects?

Have policies on the use of information captured via the FRS been

thoughtfully determined and explicitly stated?

Freedom and Autonomy

In asking how facial recognition technology affects freedom and autonomy, the

concern is constraints it may impose on peoples capacity to act and make

decisions (agency), as well as to determine their actions and decisions according

to their own values and beliefs.


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Human Difficulties with Facial Recognition Surveillance

Inherent Operator Limitations

Humans are not good at recognizing faces of people they do not know .

Operator Overload

Vast amounts of information

Limited attention span

Limited accuracy

Operator Reliability Dedication

Honesty

People are generally very good at recognizing faces that they know. However,

people experience difficulties when they perform facial recognition in a

surveillance or watch post scenario. Several factors account for these difficulties:

most notably, humans have a hard time recognizing unfamiliar faces.Combined with relatively short attention spans, it is difficult for humans to pick

out unfamiliar faces.


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Technical Difficulties with Facial Recognition Surveillance

Finding Faces Uncontrolled background

Subjects non-cooperation

Subject not looking at camera

Subject wearing hat, sunglasses, etc.

Moving target

Identifying Faces

Uncontrolled environmental conditions

Lighting (shadows, glare) Camera angle

Image resolution

Machines also experience difficulties when they perform facial recognition in a

surveillance or watch post scenario.A leading biometrics expert, has explained that

performing facial recognition processes with relatively high fidelity and at long

distances remains technically challenging for automated systems.

At the most basic level, detecting whether a face is present in a given electronic

photograph is a difficult technical problem. A expert has noted that subjects should

ideally be photographed under tightly controlled conditions. For example, each

subject should look directly into the camera and fill the area of the photo for an

automated system to reliably identify the individual or even detect his face in the

photograph.

The Facial Recognition Vendor Test 2000 study makes clear that the technology

is not yet perfected. This comprehensive study of current facial recognition

technologies, sponsored by the Department of Defense (DoD) , the DefenseAdvanced Research Projects Agency (DARPA), showed that environmental factors

such as differences in camera angle, direction of lighting, facial expression, and

other parameters can have significant effects on the ability of the systems to

recognize individuals


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How to Reduce Difficulties

Finding and Identifying Faces

Maximize control of subjects pose

Maximize control of environment

Backup Checks

Biometric system only shows probable matches

Human operator should verify potential matches

By controlling a persons facial expression, as well as his distance from thecamera, the camera angle, and the scenes lighting, a posed image minimizes the

number of variables in a photograph. This control allows the facial recognition

software to operate under near ideal conditions greatly enhancing its accuracy.

Similarly, using a human operator to verify the systems results enhances

performance because the operator can detect machine-generated false alarms.

Open questions and speculations (what about the future?)

6. How they are different

There are several of the important ways in which the Wi-Fi and 3G approachesto offering broadband wireless access services aresubstantively different.

6.1. Current business models/deployment are different

3G represents an extension of the mobile service-provider model. This is the technology of choice forupgrading existing mobile telephone services toexpand capacity and add enhanced services.


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In contrast, Wi-Fi comes out of the datacommunications industry (LANs) which is abyproduct of the computer industry.

The basic business model is the telecommunicationsservices model in which service providers own andmanage the infrastructure (including the spectrum)and sell service on that infrastructure.

In contrast the basic business model is one ofequipment makers who sell boxes to consumers. The

services provided by the equipment are free to theequipment owners.

With respect to deployment, 3G will requiresubstantial investment in new infrastructure toupgrade existing 2G networks, however, whendeployed by an existing mobile provider, much of

the 2G infrastructure (e.g., towers and backhaulnetwork) will remain useable. For WiFi, it is hopedthat deployment can piggyback on the large existingbase of WLAN equipment already in the field. In bothcases, end-users will need to buy (or be subsidized)to purchase suitable interface devices (e.g., PC cardsfor 3G or WiFi access).

In contrast to 3G, Wi-Fi wireless access can emergein a decentralized, bottomup fashion (although it isalso possible for this to be centrally coordinated anddriven by a wireline or mobile service provider).While the


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prevailing business model for 3G services andinfrastructure is vertically integrated, this need notbe the case for WiFi. This opens up the possibility ofa more heterogeneous and complex industry value

chain.

6.2 Spectrum policy and management

One of the key distinctions between 3G and WiFi thatwe have only touched upon lightly thus far is that 3Gand other mobile technologies use licensedspectrum, while WiFi uses unlicensed shared

spectrum. This has important implications for(1) cost of service;(2) quality of service (QoS) and congestionmanagement; and(3) industry structure.

First, the upfront cost of acquiring a spectrum licenserepresents a substantial share of the capital costs of

deploying 3G services. This cost is not faced by Wi-Fiwhich uses the shared 2.4GHz unlicensed, sharedspectrum. The cost of a spectrum license representsa substantial entry barrier that makes it less likelythat 3G services (or other services requiring licensedspectrum) could emerge in a decentralized fashion.

Of course, with increased flexibility in spectrum

licensing rules and with the emergence of secondarymarkets that are being facilitated by these rules, it ispossible that the upfront costs of obtaining aspectrum license could be shared to allowdecentralized infrastructure deployment to proceed.


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Under the traditional licensing approach, thelicensing of the spectrum, the construction of thenetwork infrastructure, and themanagement/operation of the service were all

undertaken by a single firm. Moreover, rigidlicensing rules (motivated in part by interferenceconcerns, but also in part, by interest group politics)limited the ability of spectrum license holders toflexibly innovate with respect to the technologiesused, the services offered, or their mode ofoperation. In the face of rapid technical progress,changing supply and demand dynamics, this lack of

flexibility increased the costs and reduced theefficiency of spectrum utilization.

Second, while licensed spectrum is expensive, it doeshave the advantage of facilitating QoS management.With licensed spectrum, the licensee is protectedfrom interference from other service providers. This

means that the licensee can enforce centralizedallocation of scarce frequencies to adopt thecongestion management strategy that is mostappropriate.

In contrast, the unlicensed spectrum used by Wi-Fiimposes strict power limits on users (i.e.,responsibility not to interfere with other users) and

forces users to accept interference from others. Thismakes it easier for a 3G provider to market a servicewith a predictable level of service and to supportdelay-sensitive services such as real-time telephony.


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In contrast, while a Wi-Fi network can address theproblem of congestion associated with users on thesame Wi-Fi network, it cannot control potentialinterference from other Wi-Fi service providers or

other RF sources that are sharing the unlicensedspectrum (both of which will appear as elevatedbackground noise). This represents a seriouschallenge to supporting delay-sensitive services andto scaling service in the face of increasingcompetition from multiple and overlapping serviceprovider.

Third, the different spectrum regimes have directimplications for industry structure. For example, theFreeNet movement is not easily conceivable in the3G world of licensed spectrum. Alternatively, itseems that the current licensing regime favorsincumbency and, because it raises entry barriers,may make wireless-facilities-based competition lessfeasible.

6.3 Status of technology development different

The two technologies differ with respect to their stage ofdevelopment in a number of ways. These are discussed inthe following subsections.

6.3.1. Deployment status

In most OECD countries, cell phone penetration of 2Gservices is quite high, and consumers have a choiceamong multiple facilities-based providers in mostmarkets. Additionally, most of the 2G mobile serviceproviders have announced plans to offer 3G


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broadband data services. Nevertheless, 3G servicesare emerging only slowly. There are a number ofreasons for this, including the high costs of obtaining3G licenses, the lack of 3G handsets, increased

deployment cost expectations, and diminishedprospects for short-term revenue.

In contrast, we have a large installed base of Wi-Finetworking equipment that is growing rapidly as WiFivendors have geared up to push wireless homenetworks using the technology. The large installedbase of Wi-Fi provides substantial learning, scale,

and scope economies to both the vendor communityand end-users. The commoditization of Wi-Fiequipment has substantially lowered prices andsimplified the installation and management of WiFinetworks, making it feasible for non-technical homeusers to self-install these networks.

However, although there a large installed base of Wi-Fi

equipment, there has been only limited progress indeveloping the business models and necessary technicaland business infrastructure to support distributed serviceprovisioning. In addition, many of the pioneers in offeringwireless access services such as Mobilstar and Metricomwent bankrupt in 2001 as a consequence of the generaldownturn in the telecom sector and the drying up ofcapital for infrastructure investment.

6.3.2. Embedded support for services

Another important difference between 3G and WiFi istheir embedded support for voice services. 3G wasexpressly designed as an upgrade technology for


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wireless voice telephony networks, so voice servicesare an intrinsic part of 3G. In contrast, WiFi providesa lower layer data communications service that canbe used as the substrate on which to layer services

such as voice telephony. For example, with IPrunning over WiFi it is possible to support voice-over-IP telephony. However, there is still great marketuncertainty as to how voice services would beimplemented and quality assured over WLANnetworks.

Another potential advantage of 3G over Wi-Fi is that

3G offers better support for secure/privatecommunications than does Wi-Fi. However, thisdistinction may be more apparent than real.

First, we have only limited operational experiencewith how secure 3G communications are. Hackersare very ingenious and once 3G systems areoperating, we will find holes that we were not

previously aware of.Second, the security lapses of Wi-Fi have attracted

quite a bit of attention and substantial resources arebeing devoted to closing this gap. Although wirelesscommunications may pose higher risks to privacy(e.g., follow-me anywhere tracking capabilities) andsecurity (i.e., passive monitoring of RF transmissionsis easier) than do wireline networks, we do not

believe that this is likely to be a long-termdifferentiating factor between 3G and Wi-Fitechnologies.

6.3.3. Standardization


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It is also possible to compare the two technologieswith respect to the extent to which they arestandardized. Broadly, it appears that the formalstandards picture for 3G is perhaps more clear than

for WLAN. For 3G, there is a relatively small family ofinternationally sanctioned standards, collectivelyreferred to as IMT-2000.36 However, there is stilluncertainty as to which of these (or even if multipleones) will be selected by service providers.

In contrast, Wi-Fi is one of the family of continuouslyevolving 802.11x wireless Ethernet standards, which

is itself one of many WLAN technologies that areunder development. Although it appears that Wi-Fi isemerging as the market winner, there is still asubstantial base of HomeRF and other open standardand proprietary technologies that are installed andcontinue to be sold to support WLANs. Thus, it mayappear that the standards picture for WLANs is lessclear than for 3G, but the market pressure to select

the 802.11x family of technologies appears muchless ambiguousat least today.

Because ubiquitous WLAN access coverage would beconstructed from the aggregation of manyindependent WLANs, there is perhaps a greaterpotential for the adoption of heterogeneous WLANtechnologies than might be the case with 3G. With

3G, although competing service providers may adoptheterogeneous and incompatible versions of 3G,there is little risk that there will be incompatibilitieswithin a carriers own 3G network. Of course in thecontext of a mesh of WLANs, reliance on IP as thebasic transport layer may reduce compatibility issues


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at the data networking level, although these could besignificant at the air interface (i.e., RF level). Unlesscoordinated, this could be a significant impedimentto realizing scale economies and network externality

benefits in a bottomup, decentralized deployment ofWi-Fi local access infrastructure.

6.3.4. Service/business model

3G is more developed than Wi-Fi as a business andservice model. It represents an extension of theexisting service-provider industry to new services,

and as such, does not represent a radical departurefrom underlying industry structure. The key marketuncertainties and portions of the valuation thatremain undeveloped are the upstream equipmentand application/content supplier markets andultimate consumer demand.

In contrast, Wi-Fi is more developed with respect to

the upstream supplier markets, at least with respectto WLAN equipment which has becomecommoditized. Moreover, consumer demandcertainly business demand and increasinglyresidential broadband home user demandfor WLANequipment is also well established. However,commercialization of Wi-Fi services as a accessservice is still in its early stages with the emergence

of Boingo and others.

Of course, both 3G and WiFi access face greatsupplier and demand uncertainty with respect towhat the next killer applications will be and how


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these services may be used once a rich set ofinteractive, multimedia services become available.

There are also some form factor issues that may

impact the way these services will be used. Initially,it seems likely that the first 3G end-user devices willbe extensions of the cell phone while the first Wi-Fiend-user devices are PCs. Of course, there are also3G PC cards to allow the PC to be used as aninterface device for 3G services, and with theevolution of Internet appliances (post- PC devices),we should expect to see new types of devices

connecting to both types of networks.

There are good reasons to believe that it will still be some time before FRT will be

able to identify a face in the crowd (in uncontrolled environments) with any

reasonable level of accuracy and consistency.

It might be that this is ultimately an unattainable goal, especially for larger

populations. Not because the technology is not good enough but because there is

not enough information (or variation) in faces to discriminate over large

populationsi.e. with large populations it will create many biometric doubles that

then need to be sorted out using another biometric.

This is why many researchers are arguing for multi-modal biometric systems.

Thus, in the future we would expect an increased emphasis on the merging of

various biometric technologies. For example, one can imagine the merging of face

recognition with gait recognition (or even voice recognition) to do identification ata distance.

It seems self-evident that these multi-modal systems are even more complex to

develop and embed in operational context than single mode systems. It is our view

that the increasing reliance on biometric and pattern recognition technologies do

represent a significant shift in the way investigation and security is conducted.


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There is an ongoing need to evaluate and scrutinize biometric identification

systems given the powerful nature of these technologiesdue to the assumption

that falsification is either impossible or extremely difficult to do.

End of report

CONCLUSION

There is the risk with FRT that individuals are treated as guilty until proven

innocent. In an identification scenario, we recommend that all matches be treated,


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in the first instance, as potential false positives until verified by other independent

sources (such as attributed and biographical identifiers). This underscores the fact

that the FRS must form part of an overall identity management program within a

security and intelligence infrastructure. Identity management and security cannot

be delegated to FRT. It can only act in support of specific targeted security and

intelligence activities. Further, how one deals with matches and alarms must be

suitable for the context. For example, one might have a very different set of

practices in an airport, casino, or a prison. This means that one needs to consider

carefully the timeframe, physical space, and control over the subject as they flow

through the system.

REFERENCES http://www.amazon.com/exec/obidos/tg/detai...il/-/04713091

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http://www.amazon.com/exec/obidos/tg/detail/-/0471309141http://www.amazon.com/exec/obidos/tg/detail/-/0471309141http://www.amazon.com/exec/obidos/tg/detail/-/0471309141http://www.amazon.com/exec/obidos/tg/detail/-/0471309141


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Seminar on Face Recognigion System

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