biometrics

CC-9013 Biometrics

Dr. C. Saravanan

NIT Durgapur

[email protected]

Introduction

• Biometrics are automated methods ofrecognizing a person based on a

• Physiological or

• Behavioral characteristic.

• The features measured are face, fingerprints, hand geometry, handwriting, iris, retinal, vein, and voice.

Dr. C. Saravanan, NIT Durgapur,

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Fingerprint


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Iris and Retina


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Hand Geometry


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Handwriting


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Facial Recognition


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Vein


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Need

• Security breaches and transaction fraud

increases.

• Biometric technologies are becoming the

foundation of an extensive array of highly

secure identification and personal

verification solutions.

• Confidential financial transactions and

Personal data privacy.


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Applications

• Biometric-based authentication

applications include

• workstation, network, and domain access,

• single sign-on, application logon,

• data protection,

• remote access to resources,

• transaction security and Web security.


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Basic Image Operations

• Enhancement

• Filter

• Edge Detection

• Localisation

• Smoothning

• Sharpning

• Thresholding


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Enhancement

• A process of enhancing the visual quality of images due to nonideal image acquisition process (e.g., poor illumination, coarse quantization etc.)

• No reference (original) image is available for comparison

• Human vision system (HVS) is the JUDGE.


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Enhanced Image


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Technique Types

• Point operations

• Histogram Equalization

• Unsharp masking

• Homomorphic filtering


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Point operations

• Point operations are zero-memoryoperations where a given gray level x∈[0,L] is mapped to another gray level y∈[0,L] according to a transformation.

• Based only on the intensity of single pixels.

• Linear

• Applications - Contract Enhancement / Feature Enhancement


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Important point operations

• Image negatives

• Contrast stretching

• Gray-level slicing

• Bit-plane slicing


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Image negatives

• The transformation is very simple :

s=T(r)

T(r)=(L-1)-r

L is the number of gray levels.

The result of this transformation is that low intensities are made high and vice versa.


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Contrast stretching

• Contrast stretching (often called normalization) attempts to improve the contrast in an image by `stretching' the range of intensity values to a desired range of values.

Pout=(Pin-c)((b-a)/(d-c)) + a

8-bit graylevel images the lower and upper limits might be 0 and 255, a & b. lowest and highest pixel values currently present in the image c & d.


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Gray-level slicing

• Give a high value for all the gray-levels in

the specified range and a very low value

for all the other gray-levels.

Bit-plane slicing

• The intensity of each pixel of an image is

defined by several bits - highest order bits

are dominant.


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Histogram Equalisation

• A histogram with a small spread has low

contrast

• A histogram with a wide spread has high

contrast

• An image with its histogram clustered at

the low end of the range corresponds to a

dark image


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Histogram Equalisation steps

1. Find the running sum of the histogram values

2. Normalize the values from step 1 by dividing by the total number of pixels

3. Multiply the values from step 2 by the maximum gray level value and round to the closest integer

4. Map the gray level values to the results from step 3 using a one-to-one correspondence


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Histogram Example

Step 1:

Gray level Number of pixels Running Sum

value (Histogram values)

0 10 10

1 8 10+8=18

2 9 10+8+9=27

3 2 29

4 14 43

5 1 44

6 5 49

7 2 51


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Example continues ...

Step 2: Normalizing by dividing by the total number of pixels (51) we get 10/51, 18/51, 27/51, 29/51, 43/51, 44/51, 49/51, 51/51

Step 3: Multiply by the maximum gray level value (7) and round we obtain 1, 2, 4, 4, 6, 6, 7, 7

Step 4: Map the original value to the results from step 3


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Histogram equalisation


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Unsharp masking

• Combine histogram modification and

filtering operations.

Input Image -> filter -> Histogram

modification -> output image


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Homomorphic filter

• Simultaneously normalizes the brightness across an image and increases contrast.

• To make the illumination of an image more even, the high-frequency components are increased and low-frequency components are decreased, because the high-frequency components are assumed to represent mostly the reflectance in the scene (the amount of light reflected off the object in the scene), whereas the low-frequency components are assumed to represent mostly the illumination in the scene.


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Frequency Domain Methods

• Compute Fourier Transform of the image

to be enhanced.

• Multiply the result by a filter

• Take the inverse transform to produce the

enhanced image

• Will diminish camera noise, spurious pixel

values, missing pixel values etc.


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Neighbourhood Averaging

• Smooth Image F(x,y) = Average pixel

value in a neighbourhood of I(x,y)

• For example, 3 x 3 neighbourhood

• Each pixel value is multiplied by 1/9

• Sum of 9 pixel value is the output


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Edge Preserving

• Also called Median Filtering

• Median of the neighbourhood pixel values

• More like neighbours

• Edges are preserved


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Image Sharpening

• Highlighting fine detail in the image

• Enhance detail that has been blurred


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Edge Detection

• Identifying and Locating sharp

discontinuities in an image.

• Discontinuities are abrupt changes in pixel

intensity.

• Sobel operator / filter

• Canny edge operator


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Sobel Operator

• 3 x 3 convolution kernels convolved with original image.

• * denotes convolution operation

AGx *

101

202

101

AGy *

121

000

121


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Canny Edge Operator

1. Smoothing: Blurring of the image to remove noise.

2. Finding gradients: The edges should be marked where the gradients of the image has large magnitudes.

3. Non-maximum suppression: Only local maxima should be marked as edges.

4. Thresholding: Potential edges are determined by thresholding.

5. Edge tracking by hysteresis: Final edges are determined by suppressing all edges that are not connected to a very certain (strong) edge.


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Smoothing

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1B

The image is first smoothed by applying a Gaussian filter.


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Finding gradients

• Finds edges where the grayscale intensity

of the image changes the most.

• These areas are found by determining

gradients of the image

• Gradients at each pixel in the smoothed

image are determined by applying Sobel-

operator


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Non-maximum suppression

• Convert the blurred edges in the image of the gradient magnitudes to “sharp” edges.

• This is done by preserving all local maxima in the gradient image, and deleting everything else.

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6754

4532


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Thresholding

• The edge-pixels remaining will probably be

true edges in the image.

• But some may be caused by noise or color

variations.

• Discern between these would be to use a

threshold.

• The Canny edge detection algorithm uses

double thresholding, Strong and Weak.


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Edge Tracking by Hysteresis

• Strong edges are included in the final

edge image.

• Weak edges are included if and only if

they are connected to strong edges.


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Biometric Identification

• Biometric identification compares a biometric "signature" to all the records stored in a database to determine if there is a match (1 : N).

• Because it requires comparing each existing record in the database against the new biometric characteristic, it can be slow and is usually not suitable for real-time applications such as access control or time and attendance.


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Biometric Identification

• Biometric identification used most

frequently in such applications as law

enforcement — for instance, the

comparison of a fingerprint from a crime

scene to a database of prints collected

from convicted criminals.


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Biometric Verification

• Biometric verification compares a newly-scanned biometric characteristic to a measurement previously collected from that same person to verify that individual's identity (1 : 1).

• For instance, when an employee is hired, that employee's fingerprint will be enrolled into the company's biometric time and attendance system.


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FAR

False Acceptance Rate (FAR)

• is the measure of the likelihood that the biometric security system will incorrectly accept an access attempt by an unauthorized user.

• is stated as the ratio of the number of false acceptances divided by the number of identification attempts.


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FRR

False Recognition Rate (FRR)

• is the measure of the likelihood that the biometric security system will incorrectly reject an access attempt by an authorized user.

• is stated as the ratio of the number of false rejections divided by the number of identification attempts.


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Biometric System Design Issues

• System Architecture

– Centralised / Distributed Server

– Client Computer

– Device at User End

• Hardware & Software Implementation

– Sample Acquitision

– User Interface

– Biometric Processing Components


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BSD Issues (continues...)

– Communication Channels

– Database Design

– Interoperability

• Administration Policy

– Integrety of Enrollment

– Quality of Enrollment Samples

– System Configuration

– Exception Handling

– Privacy Measures


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Positive / Negative Identification

• False Non-Match Rate (FNMR)

• False March Rate (FMR)

• False Reject Rate (FRR)

• False Accept Rate (FAR)

• False Positive Identification Rate (FPIR)

• False Negative Identification Rate (FNIR)


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Biometric System Security

• Includes IT Security

• Earlier related financial

• At present, Passports, Visas, etc.

– Biometric Security Evaluation

– Biometric Transaction Security

– Protection of Biometric Data


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Authentication Protocol

• is a type of cryptographic protocol with the purpose of authenticating entities wishing to communicate securely.

• AKA, CAVE-based_authentication, Challenge-handshake authentication protocol (CHAP), CRAM-MD5Diameter, Digest, Extensible Authentication Protocol (EAP), Host Identity Protocol (HIP), Kerberos, MS-CHAP, LAN Manager, NTLM, Password-authenticated key agreement protocols, Password Authentication Protocol (PAP), Protected Extensible Authentication Protocol (PEAP), Protocol for Carrying Authentication for Network Access (PANA), RADIUS, Secure Remote Password protocol (SRP), TACACS and TACACS+, RFID-Authentication Protocols, Woo Lam 92 (protocol)


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Kerberos

• MIT developed Kerberos

• Kerberos (or Cerberus - Greek monstrous three-headed guard dog)

• works on the basis of 'tickets' to allow nodes communicating over a non-secure network to prove their identity to one another in a secure manner.

• protected against eavesdropping and replay attacks.


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Matching Score Distribution

• A matching algorithm was defined as an algorithm that make a decision about genuine or impostor nature of a comparison between two templates.

• In the first step an Evaluation Algorithm assigns a similarity score to the comparison. That similarity score is a value on the range (0..1) and as higher be the score value more similar the images.

• The second step decides if the comparison is genuine or impostor using a frontier threshold or decision threshold (DT).


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Receiver Operating Characteristic

(ROC)

• accepted method for summarizing the performance of imperfect pattern matching systems.

• parametrically as a function of the decisionthreshold,

• the rate of “false positives” on the x-axis

• the rate of “true positives” on the y-axis

• ROC curves are threshold independent


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Detection Error Trade-off (DET)

• modified ROC curve

• plots error rates on both axes

• giving uniform treatment to both types of

error

• distinguishes different wellperforming

systems more clearly


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Expected Overall Error

gi TFNMRTFMRTE )()()(

• Expected overall error takes into account the possibility of different FM and FNM and is given as

where,

T=threshold

pi=probability of a random user being an imposter.

pg=probability of a random user being genuine.


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Equal Error Rate (EER)

• EER is the value where FMR and FNMR

are equal.

• Lower be the EER the lower error rate of

the algorithm.

• Select the EER score value as Decision

Threshold (DT) is frequently a good

decision for a regular biometric

application.


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Myths & Misrepresentations

• Biometric “X” is best for all applications

• Biometric “X” is unique for each individual

• A single number quantifies system

accuracy

• System is “ plug and play “

• Real accuracy performance can be

predicted


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• The vendors reporting best FAR and FRR has the “most accurate system”

• Multiple biometrics outperform single biometrics

• “Our biometric system does not use a decision threshold”

• “Our feature extractor can be used with any match engine”

• Large templates mean better accuracy

• Face recognition prevents terrorism


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• Biometrics means 100 percent security

• Biometric systems invade our privacy

• Biometric sensors are unhygienic or

otherwise harmful


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Selection of Suitable Biometric

• Ethnic Background

– who will be the users of the biometric system

– education levels and a variety of attitudes

• Employee Education

– technical background of the users.

– higher the technological background less

training required


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• Frequency of Use

– some biometric systems are more suitable for

high frequency of usage

• User characteristics

– will the users be in a hurry and possibly be a

little bit impatient (public restroom)


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Biometric Attributes

1. Universal:

Every person must possess the characteristic/attribute.

The attribute must be one that is universal and seldom lost to accident or disease.

2. Invariance of properties:

They should be constant over a long period of time.

The attribute should not be subject to significant differences based on age either episodic or chronic disease.

3. Measurability:

The properties should be suitable for capture without waiting time and must be easy to gather the attribute data passively.

4. Singularity:

Each expression of the attribute must be unique to the individual. The characteristics should have sufficient unique properties to distinguish one person from any other.


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Height, weight, hair and eye color are all attributes that are unique assuming a particularly precise measure, but do not offer enough points of differentiation to be useful for more than categorizing.

5. Acceptance:

The capturing should be possible in a way acceptable to a large percentage of the population. Excluded are particularly invasive technologies, i.e. technologies which require a part of the human body to be taken or which (apparently) impair the human body.

6. Reducibility:

The captured data should be capable of being reduced to a file which is easy to handle.

7. Reliability and tamper-resistance:

The attribute should be impractical to mask or manipulate. The process should ensure high reliability and reproducibility.


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8. Privacy: The process should not violate the privacy of the person.

9. Comparable: Should be able to reduce the attribute to a state that makes it digitally comparable to others. The less probabilistic the matching involved, the more authoritative the identification.

10. Inimitable: The attribute must be irreproducible by other means. The less reproducible the attribute, the more likely it will be authoritative.


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Any Questions ?


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References

Biometrics: Identity Assurance in the Information Age, John D. Woodward

Jr.

Biometrics: Advanced Identity Verification: The Complete Guide, Julian

Ashbourn

Biometrics: Identity Verification in a Networked World, Samir Nanavati

Digital Image Processing, Gonzalez and Woods

Wikipedia


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biometrics

Education

saravanan nit durgapur

image c

graylevel slicing

enhanced image

gray level y0

memory operations

reference original image

number of gray levels