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Wikipedia on Biometrics

Biometrics (ancient Greek: bios ="life", metron ="measure") is the study of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits.

In information technology, biometric authentication refers to technologies that measure and analyze human physical and behavioural characteristics for authentication purposes. Examples of physical (or physiological or biometric) characteristics include fingerprints, eye retinas and irises, facial patterns and hand measurements, while examples of mostly behavioural characteristics include signature, gait and typing patterns. All behavioral biometric characteristics have a physiological component, and, to a lesser degree, physical biometric characteristics have a behavioral element.

Many choices...

Fingerprint Face Iris Height Voice Signature Handwriting Hand veins Facial Thermogram Keystrokes

Retina DNA Odor Gait (Walk pattern) Eye color IQ Hand geometry Ear shape ...

One is wrong here! Which one?

Why not IQ?

• IQ is used for ranking persons• different persons can have the same IQ• criteria for computing IQ can vary over time• a smart person can simulate a lower IQ• the “acquisition time” for getting the IQ is too

large• some of these may be also true for other

biometrics, but never all of them

...and becoming more

An OTOACOUSTIC EMISSION (OAE) is a sound which is generated from within the inner ear. Having been predicted by Thomas Gold in 1948, their existence was first demonstrated experimentally by David Kemp in 1978 and they have since been shown to arise by a number of different cellular mechanisms within the inner ear. Numerous studies have shown that OAEs disappear after the inner ear has been damaged, so OAEs are often used in the laboratory and the clinic as a measure of inner ear health. There are two types of otoacoustic emissions: Spontaneous Otoacoustic Emissions (SOAEs), which can occur without external stimulation, and Evoked Otoacoustic Emissions (EOAEs), which require an evoking stimulus.Recently, Beeby, Brown and White from University of Southhampton, UK, have studied the use of OAE for biometric systems (e.g. included in mobile telephones).

Two application modi

Identification Given a biometric pattern, identify the person out

of a set of n persons (1:n match) Verification

Given a biometric pattern, verify the identity of that person by comparing with a biometric template of the same person that was given before (1:1 match).

Detection? What could it mean in this context?

Several Aspects...

Universality describes how commonly a biometric is found in each individual.

Uniqueness is how well the biometric separates one individual from another.

Permanence measures how well a biometric resists aging. Collectability explains how easy it is to acquire a biometric for

measurement. Performance indicates the accuracy, speed, and robustness of

the system capturing the biometric. Acceptability indicates the degree of approval of a technology by

the public in everyday life. Circumvention is how hard it is to fool the authentication system.

Fingerprints

Impression of friction ridges of tip part of the finger.Known from history as being unique for every person.Used in legal issues for more than 100 years (first use reported 1892 by Argentine police to identify a murder).Several countries maintain large collections of fingerprints, so-called AFIS (automated fingerprint identification systems).

Some related questions

Do twins have the same fingerprint? Are the fingerprints of different fingers of the

same person different? Do the same left and right finger of the same

person have a mirrored fingerprint? Are relatives having similar fingerprints? Are the fingerprints of the same person aged

20 and aged 60 identical? Can the gender be concluded from a

fingerprint?

Some related questions

Do twins have the same fingerprint? no Are the fingerprints of different fingers of the

same person different? yes Do the same left and right finger of the same

person have a mirrored fingerprint? no Are relatives having similar fingerprints? no Are the fingerprints of the same person aged

20 and aged 60 identical? nearly Can the gender be concluded from a

fingerprint? no

Our criteria (L,M,H)

Universality?

Fingerprint: Universality

Medium! There is so-called Naegeli syndrome.

Affected persons have a dimished function of the sweat glands, therefore, they are not producing a fingerprint.

Injuries may also affect the fingerprint pattern.

Our criteria (L,M,H)

Uniqueness?

Fingerprint: Uniqueness

High! No two fingerprints have ever been found

identical. However, between features like minutiae

position there might be some similarity (twins).

Our criteria (L,M,H)

Permanence?

Fingerprint: Permanence

High! Despite of affections during lifetime (injuries),

the fingerprint pattern is preserved during skin alterations during lifetime.

Our criteria (L,M,H)

Collectability?

Fingerprint: Collectability

Medium! Need special devices and procedures to

visualize a fingerprint. Comparison of two fingerprints is very hard

for the naked eye, and needs training and expertize.

Our criteria (L,M,H)

Performance?

Fingerprint: Performance

High! Accuracy: allows for the identification of a

fingerprint among several thousands of fingerprints (but not millions!)

Speed: Verification is today possible “on-board”, needs a few millisecond on modern computer (acquisition takes longer!)

Robustness: error measures state a FAR at 1% for a FRR of 0.1%. What does this mean? Later!

Our criteria (L,M,H)

Acceptability?

Fingerprint: Acceptability

Medium! Usual association of taking a fingerprint is

related to crime cases. Many countries pose data protection

regulations on the collection of fingerprints (often only databases of criminals and public authorities are allowed to be collected).

The fingerprint pattern can be easily “stolen.”

Our criteria (L,M,H)

Circumvention?

Fingerprint: Circumvention

Medium! (some say High) Gels can be used to produce a copy of the

ridge pattern of a person. Finger gloves also fake human warmth.

Fingerprint: Bonus

Do other animals have fingerprints?

More similar to human than primates: from which animal is the fingerprint to the left?

How does it work?

Fingerprint Sensors

optical

capacitive

thermal

Biometric workflow

Minutia and Terminals

Unique features of a fingerprint pattern are the location of forkings of ridges (minutiae) and their endpoints (terminals).

Most persons have between 20 and 80 such positions.

The set of all minutiae and terminals of a given fingerprint is called a template. It is used for comparing two fingerprints.

Forkings and Endings

Mahadik, S., Narayanan, K., Bhoir, D. V., and Shah, D. 2009. Access Control System using fingerprint recognition. In Proceedings of the international Conference on Advances in Computing, Communication and Control (Mumbai, India, January 23 - 24, 2009). ICAC3 '09. ACM, New York, NY, 306-311. DOI= http://doi.acm.org/10.1145/1523103.1523166

Fingerprint Scan

Fingerpint image, as received from sensor. First it needs to enhance the contrast of the image. The goal is to enhance the ridge structures of the fingerprint.

Image Enhancement

In smaller areas of the image, the ridges appear to be parallel straight lines – thus having frequency and orientation. A method called Fourier Transformation can be used to filter only the lines having the major frequency and orientation.

orientationorientation

frequencyfrequency

Binarization

All pixels in the image are either assigned Black (0) or White (255) by using a threshold.

Orientation Field

For some points, the direction of the line is represented by an arrow. This also helps to identify the fingerprint class (but not used in this system).

Region of Interest (ROI)

The further processing has to be restricted to some part of the image. Only in this part, the minutia and terminals can be safely extracted. Other parts, out of the border, will not provide a good enough quality.

Thinning

The ridges (lines in the image) are “eroded,” until only a line of one pixel width remains – but while preserving the topological structure of the connected parts of the binary image.

There are several algorithms for such a Thinning, mostly from the so-called Mathematical Morphology, a discipline of image processing.

Candidate Points

From the former result, candidates for minutia and terminal positions can be found by looking into the neighborhood of each white point.

However, it can be seen that there are too many candidates, some only caused by artefacts of the thinning process. Using the ROI, and other information, the wrong candidates can be removed.

Final Result

Biometric workflow

Affine Matching

Matching

Template to test Stored Template

Assumed Corresponding Points

Template to test Stored Template

Testing

Template to test Stored Template

according to assumed correspondance, points should be e.g. about here in the stored template

according to assumed correspondance, points should be e.g. about here in the stored template

one nearly matches, the other do notone nearly matches, the other do not

Better assumed corresponding points

Template to test Stored Template

Better assumed corresponding points

Template to test Stored Template

now, nearly each estimated position is about correct

now, nearly each estimated position is about correct

Last but not least...

• the pair of points, for which the number of matching other points is highest, is found (A,B)

• the ratio for these matching points is determined (80%)

• if it is larger than a threshold, than the system replies that both fingerprints are from the same person (same finger) (80% > 70% -> ok)

• note that this threshold is important for the correct decision of the system

Performance

A biometric system can make two kinds of errors, false acceptances and false rejections – the best trade-off between them is called equal error rate and an objective measure for biometric system performance

However, the weighting of these two errors might be different (forgeries are not as likely as correct transactions)

False Acceptance

• the template to test is from person A, the stored template from person B

• the system replies that the fingerprints are the same (and the door opens...)

• this is a False Acceptance• the ratio among a number of test then is

called False Acceptance Rate (FAR)

False Rejection

• the template to test is from person A, the stored template also from person A

• the system replies that the fingerprints are different (and keeps the door closed...)

• this is a False Rejection• the ratio among a number of test then is

called False Rejection Rate (FRR)

Equal Error Rate

• but the reply of the system depends on the threshold

• assume the treshold t varies from 0% to 100%

• for 0%, any match is larger, and the system will always ACCEPT, so FAR will be 100%, and FRR will be 0%

• for 100%, the system will never ACCEPT, thus FAR is 0%, and FRR is 100%

• if threshold goes from 0% to 100%, the FAR line will decrease from 100% to 0%, the FRR will increase from 0% to 100%

• thus, both lines will intersect for some threshold

• this is the so-called equal error rate (EER)

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