inter bio paper

7/29/2019 Inter Bio Paper

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Overview of Biometrics for Secure Identity Verification

Deepali P. Chaudhari1, Prof. Nareshkumar D.Harale2

1PG student, Department of computer, M.G.M.C.E.T.Kamothe, Navi Mumbai

[email protected] Of Department, Department of computer, M.G.M.C.E.T.Kamothe, Navi Mumbai

[email protected]

Abstract:

As online security threats continue to spread,protecting valuable data becomes one of the securitychallenges businesses face in todays business-to-customer (B2C) and business-to-business (B2B) e-commerce. Biometrics technology shows increased

promise in enterprise network security. It will play avital role as system developers fortify the security

apparatus of its organization. In this paper, weanalyze biometrics technologies and describetechniques that can be utilized to decrease the

probabilities of online attacks.

Key Words: - Biometric, Filter Based, Gabor Based

Introduction:

Biometrics based personal authenticationsystems that use physiological and/or behavioral traits

(e.g. fingerprint, face, iris, hand geometry, signature,

voice, etc...) of individuals have been shown to bepromising candidates for either replacing oraugmenting these traditional systems. They are based

on entities (traits) that are actually bound with the

individual at a much deeper level than, for Eg.passwords and ID cards. As a result, they are more

reliable since biometric information can not be lost,

forgotten, or guessed easily. They lead to increaseduser convenience there is nothing to remember or

carry. They improve the authentication accuracy.

Single biometric may not be able to achieve

the desired performance requirement in real worldapplications. One of the methods to overcome these

problems is to make use of multimodal biometric

authentication systems, which combine informationfrom multiple modalities to arrive at a decision.

Studies have demonstrated that multimodal biometric

systems can achieve better performance comparedwith uni-modal systems.

Any aspect of human physiology or behavior

that can be accepted as a biometric should satisfy fiveproperties described by Clarke which are as follows:

a) Universality: Every person should have the

biometric characteristic.

b) Uniqueness: No two persons should be the same interms of the biometric characteristic

c) Permanence: The biometric characteristic should

be invariant over time.d) Collectability: The biometric characteristic should

be measurable with some practical sensing device. e)

Acceptability: The public should have no strongobjection to the measuring or collection of the

biometric.

Components of Biometric system

Fig. 1. Components of Biometric System

1) Capture the chosen biometric (requires an

appropriate capture device)2) Process the biometric and extract and enroll the

biometric template

3) Store the template in a local repository, centralrepository, and/or portable token such as a smart

card

4) Live-scan the chosen biometric5) Process the biometric and extract the biometric

template


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6) Match the scanned biometric against the stored

templates

7) Provide a matching score and interface withapplications

8) Record a secure audit trail with respect to system

use

Types of Biometrics:A wide variety of systems require reliable

personal recognition schemes to either confirm or

determine the identity of an individual requestingtheir services. Examples of such applications include

secure access to buildings, computer systems, laptops,

cellular phones and ATMs. In the absence of robustpersonal recognition schemes, these systems are

vulnerable to the wiles of an impostor. Biometric

recognition, or simply biometrics, refers to the

automatic recognition of individuals based on theirphysiological and/or behavioral characteristics.

Biometrics is the study of distinguishable

physical, biological or behavioral characteristics usedfor the identification of humans and animals. Physical

characteristics include items such as fingerprints,

hand geometry, iris or retina patterns, and facialfeatures. Biological characteristics would include

DNA and specific fluid or tissue chemistries.

Behavioral characteristics include things such as asignature, voice and speech patterns. Both behavioral

and physical characteristics can change over time, butusually, physical features have been found to be morereliable, changing only in the long term.

In the simplest terms, biometrics technologies

rely on measuring one or more of these

distinguishable characteristics and determining if theybelong to an individual whose Characteristics have

been previously stored in a database. More

specifically, biometrics is defined as the automaticidentification or identity verification of an individual

using computer technology in a noninvasive way to

match patterns of live individuals in real time againstenrolled records.

Physical characteristics include items such as

fingerprints, face, Iris, hand geometry, signature, and

voice.We first focus on:

1. Fingerprints recognition

2. Face recognition3. Iris patterns

Fig 1: Biometric system components and flow diagram.

1) Fingerprints recognition:-

In the fingerprint-based scheme

during enrollment the user presents her fingerFto thesensor, whose output Fs (e.g., fingerprint image) is

passed through a feature extractor to arrive at the

template Ft, which, along with the identity I of the

user, is saved in a database (note that this databasecan be central, such as a law enforcement database or

local, such as a smart-card issued to an individual)During verification, the user's fingerprint is capturedagain, and the generated template Fv; t is matched

against the database templateFtcorresponding to the

claimed identity I. If these two representations areclose enough", the matcher outputs a Yes" decision

This decision is generally based on a similarity

(dissimilarity) measure: if the similarity

(dissimilarity) score between two representations ishigher (lower) than a specific threshold T, a Yes

decision is output, otherwise, a No decision is output

Conversely, during identification, the user's templategenerated online, Fi; t, is matched against all the

database templates. If there is a match, the matcher

outputs the associated identityIof the user.Four technologies are in use to extract

fingerprint images.

These are as listed below

a) Optical Sensors: These sensors capture

visual image of finger surface. Finger touches the

surface of a prism and LEDs provide a light source

Image is captured after its total internal reflection in

the prism, by a Charge Coupled Device IC (CCD-IC)or CMOS Camera. Optical sensors are reliable and

inexpensive.

b) Capacitive Sensors: These sensors scan

surface of finger using dielectric measurements to

distinguish ridges and valleys. Higher dielectricconstant of ridges results in higher capacitance than

that of valleys which contain air. Capacitive sensors


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produce better image quality over wider operating

conditions. However they are expensive, consume

more power and also do not work well with dryfingers.

c) Thermal sensors: These sensors consist of

contiguous arrangement of heating elements and

thermal sensors and capture images based ondifferentials in heat emission between the ridges and

valleys. Heat map is converted to an optical image ofridges which are cooler due to presence of sweat

pores and valleys which are warmer. Thermal sensors

are compact and inexpensive. But they consume morepower and are ineffective on warm days.

d) Radio Frequency Sensor: These sensors

scan subsurface to get a true image of the finger. They

use reflected RF beam to create an image of the layer.RF sensors are not affected by dirt or other impurities,

have improved accuracy and reliability. Also, it isvery difficult to fake the finger with this sensor as ittakes subsurface image.

2) Face Recognition:-

Face recognition has become one of the major

areas of biometric research because of its noninvasivenature and because it is a persons primary method of

personal identification. Face image acquisition is

done in the following ways

a) Single image: This consists of digital

photographs obtained using cameras or scanners.

b) Video Sequence: This is obtained from

surveillance cameras. However, due to low spatialresolution, it is not very useful for face recognition.

c) 3D Images: This is based on skin/skull

geometry and requires 3D images of the face insteadof 2D images .Newer face recognition techniques

such as Stereo, structured light and phase based

ranging are used for capturing 3D images.The fundamental principle of face recognition

is to use a special mathematical model to measure the

dissimilarity of features in the face. These algorithmscan use features, such as distance between eyes, nose,

and lips, as parameters. Structured light is used to

acquire the 3-D geometry of the face. Then, 3-D

reconstruction algorithms are used to formulate the 3-D mesh surface and the surface of the face, which is

then used for identification.

Face Recognition Approaches:

Digital Face Images:-

The face image of a person can be obtained

from a passport document by capturing a digitaimage of the photograph page via a digital camera or

a scanner. Then these photos can be compared against

live face photos of the same person acquired using a

high resolution digital camera. The methodologyproposed for passport facial matching is illustrated in

Figure 2.The salient stages of the proposed method are listed

below:1) Face Detection2) Channel Selection3) Normalization4) Watermark Removal5) Feature Extraction and Classification

3) Iris pattern :-When a subject wishes to be identified by irisrecognition system, their eye is first photographed

and then a template created for their iris region. This

template is then compared with the other templates

stored in a database until either a matching template isfound and the subject is identified, or no match is

found and the subject remains unidentified. It is

Fig 2: Overview of the methodology used when passport mug-shots areused to test the system

composed of iris image acquisition, imagepreprocessing, and feature extraction and classifier

design. The algorithm for iris feature extraction is

based on texture analysis using multi-channel Gabor

filtering and wavelet transform. Compared withexisting methods, our method employs the rich 2-D

information of the iris and is translation, rotation, and

scale invariant.


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Iris image acquisition is done in two ways

a) Daugman System: In this system, an LED

based point light source is used along with a standardvideo camera. The system captures images with the

Iris diameter between 100 to 200 pixels from a

distance of 15 to 46cm using 330mm lens. John

Daugman at the university of Cambridge computerlaboratory developed Gabor wavelet based Iris

recognition algorithm which is the basis for almost allcommercially available Iris recognition systems

b) Wildes system: This system images the

Iris with approximately 256 pixels across the diameterfrom 20cm using 80 mm lens and is area based i.e. it

captures the iris as part of a larger image which also

contains data derived from the immediately

surrounding eye region. Iris recognition based onJohn Daugmans algorithms, is used by the United

Arab Emirates (UAE) Ministry of Interior forrecognizing foreigners entering the UAE, at 35 air,land, and sea ports. Each traveler is compared against

about a million Iris codes on a watch-list through

internet links; the time required for an exhaustivesearch through the database is about 1 second. On an

average day, about 12,000 arriving passengers are

compared against the entire watch list i.e. about 12

billion comparisons per day.

Fig 3: Block diagram of a typical iris recognition System

Feature Extraction:-

The feature extraction problem can be reduced

to applying a frequency band filters which are

circularly symmetric in nature for the extraction.

a. Non filter based technique:-

Instead of using Gabor Filters, to extract thetextural features from the image we propose the use

of a non filter based technique that depends on second

order statistics of the pixel intensities. The co-occurrence matrix estimates the joint probability

distribution function of gray level pairs in an image.

The technique uses the GLCM (Grey Level Co-occurrence Matrix) of an image and it provides a

simple approach to capture the spatial relationship

between two points in a texture pattern . It is

calculated from the normalized iris image using pixels

as primary information.

b. Multi-channel Gabor filtering:-

The multi-channel Gabor filtering technique is

inspired by the psychophysical findings that theprocessing of pictorial information in the humanvisual cortex involves a set of parallel and quasi

independent mechanisms or cortical channels which

can be modeled by band pass filters.

Comparison of Biometrics methods

Table A: Comparison of Biometric

Technologies

Biometri

cs

Per

man

Perfor

manc

Uniq

uene

Acce

ptabiFingerprin H H H M

Hand

GeometryM M M M

Retinal

ScanningM H H L

Iris

ScanningH H H L

Facial

RecognitiM L L H

Dynamic

SignatureL L L H

Keystroke

DynamicsL L L M

Voice

RecognitiL L L H

H= High, M= Medium, L= Low

Selection criteria for Biometric types

1. Economic Feasibility or Cost

2. Risk Analysis

3. Perception of Users

4. Techno_Socio Feasibility

5. Security


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6. User friendly and social acceptability

7. Legal Feasibility

8. Privacy

CONCLUSION:This study has shown that biometrics is the

most accurate and secured representation of what an

element is. Its technology can isolate false positive

results, misrepresentation or creation of false identityduring an identification process. We have discussed

the usefulness of biometric technology in protectingenterprise network systems from unwanted onlineintruders. We have also identified the possible

features and characteristics of an object that can be

used in biometric technologies. Furthermore, an

enterprise authentication process that uses acombination of traditional password and biometric

fingerprint identification methodology is described.

REFERENCES[1] Theory, Applications, and Systems,

2009. BTAS09. IEEE 3rd InternationalConference On Matching Digital Face

Images Against Scanned Passport Photos.

[2] Jammi Ashok, Vaka Shivashankar,

P.V.G.S.Mudiraj An Overview of Biometricsernational Journal on Computer Science and

Engineering Vol. 02, No. 07, 2010, 2402-2408

[3] Prof. (Dr.) Dattatray V. Jadhav, Prof. Vijay M.

Mane, Review of Multimodal Biometrics:Applications, challenges and Research Areas,

International Journal of Biometrics andBioinformatics (IJBB), Volume 3, Issue 5

[4] Emmanuel Opara, Mohammad Rob, Vance

Etnyre, Biometric and Systems Security: AnOverview of End-To-End Security System

Communications of the IIMA 2006 Volume 6

Issue 2

[5] B. Andrew, A brief history of us passportPhotograph blakeandrews

blogspot.com/2009/05/brief-history-of-us-passport.html (may-2009)

[6] http://www.findbiometrics.com

[7] http://www.biometricsinfo.org

[8] http://www.biometrics.gov
http://www.findbiometrics.com/http://www.biometrics.gov/http://www.findbiometrics.com/http://www.biometrics.gov/

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