Biometrics

Posted by : Unknown Saturday, June 29, 2013

BIOMETRICS

1. OVERVIEW:

As we rapidly move towards the new millennium, Security and Access Control are becoming more important than ever before.

Passwords, though still extensively used, are fast becoming a hazard, requiring an enhanced method of security. Because these system BIOS passwords can be broken by removing the battery on the motherboard for few seconds.

Positive Identification of individuals is now a serious business considering the fact that people have to be allowed access to areas only if they are authorized.

Criminals have to be caught and proven guilty without a doubt social and medical benefits have to be paid by the state.

Newer chip designs and supporting software, has spurred the development of solutions based on these crucial needs beyond boundaries.

Parts of the human body - the hand, the iris/retina, the face and the voice, all provide a means of positive verification. Various companies using the above parts of the human body with various levels of success have made commendable progress.

Although Card Based systems have been in the market for several years now, the latest and most secure technology involves the use of the human body - both physical and behavioral - for positive verification and identification - known as Biometrics.

2. INTRODUCTION:

Biometric verification is an automated method whereby an individual’s identity is confirmed by examining a unique physiological trait or behavioral characteristic, such as a fingerprint, retina, or signature. Physiological traits are stable physical characteristics, such as palm prints and iris patterns. This

Type of measurement is essentially unalterable.

A behavioral characteristic — such as one's signature, voice, or keystroke dynamics — is influenced by both controllable actions and less controllable psychological factors. Because behavioral characteristics can change over time, the enrolled biometric reference template must be updated each time it is used. Although behavior-based biometrics can be less expensive and less threatening to users, physiological traits tend to offer greater accuracy and security. In any case, both techniques provide a significantly higher level of identification than passwords or cards alone.

Biometric traits are unique to each individual; they can be used to prevent theft or fraud. Unlike a password or personal identification number (pin), a biometric trait cannot be forgotten, lost, or stolen. Today there are over 10,000 computer rooms, vaults, research labs, day care centers, blood banks, ATMs and military installations to which access is controlled using devices that scan an individual's unique physiological or behavioral characteristics.

Biometric identifiers currently available or under development include fingerprint, face recognition, keystroke dynamics, palm print, retinal scan, iris pattern, signature, and voice pattern.

3. BIOMETRICS DEFINITION:

Biometrics, strictly speaking, refers to a science involving the statistical analysis of biological characteristics. Today, the term "biometrics" usually refers to technologies that analyze human characteristics for security purposes.

A Biometric is a unique, measurable characteristic or trait of a human being for automatically recognizing or verifying identity.

Biometric technologies, therefore, are concerned with the physical parts of the human body or the personal traits of human beings. The term "automatic" essentially means that a biometric technology must recognize or verify a human characteristic quickly and automatically, in real time.

4. TYPES OF BIOMETRICS:

The most common are:

· Physical biometrics is the eye (iris and retina), face, finger image; hand and voice (see authentication methods).

· Behavioral biometrics include typing rhythm (keystroke

dynamics) and signature.

In the security industry, biometrics is regarded as providing the highest level of security. The methods for verifying an individual's identity are commonly broken down into the following three stages:

Stage 1 (lowest level of security) — something you have, such as

Photo id.

Stage 2 (second level of security) — something you know, such as a

Password to access a computer

Or a personal identification

Number (pin) to access funds at

An ATM.

Stage 3 (highest level of security) — something you do or

Something you are, which

Comprises physiological and/or

Behavioral biometrics,

Including fingerprints,

Voiceprints, signatures, etc.

4.1. HOW IT WORKS:

All biometric systems operate in a similar fashion. First, the system captures a sample of the biometric characteristic (this is known as the enrollment process). During enrollment, some biometric systems may require a number of samples in order to build a profile of the biometric characteristic. Unique features are then extracted and converted by the system into a mathematical code. This sample is then stored as the biometric template for the enrollee. The template may reside in the biometric system itself, or in any other form of memory storage, such as a computer database, smart cards or barcodes.

In addition, the biometric system may require a trigger, or a means of tying the template to the person. For example, a personal identification number (pin) is keyed in to access the template, or a smart card storing the template is inserted into a card reader. In either case, the end user interacts with the biometric system a second time to have his or her identity checked. a new biometric sample is then taken. This is compared to the template. If the template and the new sample match, the end user is granted access. This is the basic premise of biometrics — that a person has a sample of their biometric data captured and the biometric system decides if it matches with another sample.

Because both physical and behavioral characteristics can change slightly over time (e.g., a finger can be scarred and a signature may change as a person gets older), the biometric system must allow for these subtle changes, so a threshold is set. This can take the form of an accuracy score.

In this case, comparison between the template and new sample must exceed the system's threshold before a match is recorded. In other words, if the new biometric sample is sufficiently similar to the previously stored template, the system will determine that the two do in fact match.

If not, the system will not record a match and will not identify the end user. This use of a threshold gives biometric technologies a significant advantage over passwords, pins and id badges.

With biometrics, it doesn't matter if you forget your password or lose your id. The use of a threshold affords a tremendous degree of flexibility and if the comparison between the new biometric sample and the template exceeds the state threshold, identity will be confirmed.

All biometric systems use the four-stage process of capture, extraction, comparison, and match, but employ different methods and techniques to deal with the human factor (stress, general health, working and environmental conditions and time pressures all conspire to make humans inconsistent). At the heart of the biometric system is the biometric engine, a proprietary element that extracts and processes the biometric data. This may apply an algorithm or an artificial neural network. It extracts the data, creates a template, and computes whether the data from the template and the new sample match.

The following four-stage process illustrates the way biometric systems operate:

1. Capture — a physical or behavioral sample is captured

By the system during enrollment.

2. Extraction — unique data is extracted from the sample

And a template is created.

3. Comparison — the template is then compared with a new

Sample.
4. Match/non-match — the system then decides if the features

Extracted from the new sample are a

match or a non-match.

4.2. PHYSICAL BIOMETRICS:

4.2.1. Fingerprint

In recent years, fingerprints have rallied significant support as the biometric technology that will probably be most widely used in the future. In addition to general security and access control applications, fingerprint verifiers are installed at military facilities, including the pentagon and government labs. Although machines tend to reject over 3% of authorized users, the false accept rate is less than one in a million. Today, the largest application of fingerprint technology is in automated fingerprint identification systems (AFIS) used by police forces throughout the U.S. and in over 30 foreign countries.

The fingerprint’s strength is its acceptance, convenience and reliability. It takes little time and effort for somebody using a fingerprint identification device to have his or her fingerprint scanned. Studies have also found that using fingerprints as an identification source is the least intrusive of all biometric techniques.
Verification of fingerprints is also fast and reliable. Users experience fewer errors in matching when they use fingerprints versus many other biometric methods. In addition, a fingerprint identification device can require very little space on a desktop or in a machine. Several companies have produced capture units smaller than a deck of cards.

One of the biggest fears of fingerprint technology is the theft of fingerprints. Skeptics point out that latent or residual prints left on the glass of a fingerprint scanner may be copied. However, a good fingerprint identification device only detects live fingers and will not acknowledge fingerprint copies.

4.2.2. Hand Geometry

Currently, hand geometry is employed at over 8,000 locations, including the Colombian legislature, San Francisco international airport, day care centers, welfare agencies, hospitals and immigration facilities. The advantages of a palm print are similar to the benefits of a fingerprint in terms of reliability, although palm print readers take up more space. The most successful device, the hand key, looks at both the top and side views of the hand using a built-in video camera and compression algorithms. Devices that look at other hand features are also under development by several companies, including biomet partners, pal metrics, and btg.

4.2.3. Iris patterns

The advantage of iris scanners is that they do not require the user to focus on a target, because the patterns of flecks on the iris are on the eye's surface. In fact, a video image of the eye can be taken from up to three feet away, which allows for the use of iris scanners at ATM machines. In visually impaired persons with intact irises, the iris can still be captured and encoded with iris imaging products that have active iris capture (e.g., the ATM application). Since cataracts are a malady of the lens, which is behind the iris, cataracts do not affect iris scanning in any way.

4.2.4. Retinal patterns

Retinal scans are performed by directing a low-intensity infrared light through the pupil to the blood vessel pattern on the back of the eye. Most uses of retinal scanners involve high-security access control, since they offer one of the lowest false reject rates (FRR) and a nearly 0% false acceptance rate (far). However, since retinal imaging requires a clear view of the back of the eye, cataracts can negatively impact the retinal image quality.

4.2.5. Voice patterns

The appeal of voice verification is its acceptability to users. A common concern about this biometric approach is impersonations. However, this is not a serious problem, since the devices focus on different characteristics of speech than people do. Speech patterns are formed by a combination of physiological and behavioral factors. Currently, voice verification is being used to control access to medium-security offices, labs, and computer facilities. Several providers of home confinement systems use voice verification to confirm that early parolees are at home. While voice recognition is convenient, it is not as reliable as other biometric techniques. A person with a cold or laryngitis, for example, may have problems using a voice recognition system.

4.2.6. Facial features

Facial verification and recognition is one of the fastest growing sectors of the biometrics industry. Its appeal lies in the fact that it most closely resembles the way we as humans identify one another. Most commercial efforts have been stimulated by the fast rise in multimedia video technology that is placing more cameras in the home and workplace. However, most developers have had difficulty achieving high levels of performance. Nevertheless, specific applications, such as screening welfare databases for duplicates and airport lounges for terrorists, are likely to appear in the future.

4.3. BEHAVIORAL BIOMETRICS

4.3.1. Keystroke dynamics

Keystroke dynamics, also called typing rhythms, analyze the way a user types at a terminal by monitoring keyboard input 1,000 times a second. This is analogous to the early days of the telegraph, when users identified each other by "the fist of the sender." the advantage in the computer environment is that neither enrollment nor verification detracts from the regular work flow. Despite its appeal, however, efforts at commercial technology have failed.

4.3.2. Signatures

Static signature capture is becoming quite popular as a replacement for pen and paper signing in bank card, pc and delivery-service applications (e.g., federal express). Generally, verification devices use wired pens, pressure-sensitive tablets, or a combination of both. Devices using wired pens are less expensive and take up less room but are potentially less durable. To date, the financial community has been slow to adopt automated signature verification methods for credit cards and check applications because signatures are still too easily forged. This keeps signature verification from being integrated into high-level security applications.

4.3.3. Voice recognition

4.3.4 Face Recognition

Face recognition is the means by which people have recognized one another since the beginning of civilization. Now, computers have the ability recognize faces too. For many real-world applications, there are benefits to using face recognition technology that cannot be provided by any other biometric (i.e., fingerprint, iris scan). Making use of distinctive features or characteristics of the human face, and often irrespective of facial hair or glasses, facial scan is deployed in fields as varied as physical access, surveillance, home PC access, and ATM access.

5. TECHNOLOGY:

5.1. Face Recognition

Just as with finger scan and voice scan biometrics, there are various methods by which facial scan technology recognizes people. All share certain commonalities, such as emphasizing those sections of the face which are less susceptible to alteration, including the upper outlines of the eye sockets, the areas surrounding one's cheekbones, and the sides of the mouth. Most technologies are resistant to moderate changes in hairstyle, as they do not utilize areas of the face located near the hairline. All of the primary technologies are designed to be robust enough to conduct 1-to-many searches, that is, to locate a single face out of a database of thousands, even hundreds of thousands, of faces.

5.1.1. Hardware Requirements:

Facial scan technology works well will standard off-the-shelf PC video capture cameras, and generally requires 320x240 resolution and at least 3-5 frames per second. More frames per second, along with higher resolution, will lead to better performance in verification or identification scenarios. Since such cameras can be purchased for as little as $50, and demo versions of leading vendors' software are available for free download, facial recognition is one of the few biometrics with which one can experiment on a very limited budget. For facial recognition at slightly greater-than-normal distances, there is a strong correlation between camera quality and system capabilities. An adequate video card, and sufficient processor speed, are key components of a home or office facial recognition system. For large scale, 1-to-many searches, processor speed is the critical factor.

5.1.2. Facial Scan Process Flow:

As with all biometrics, 4 steps - sample capture, feature extraction, template comparison, and matching - define the process flow of facial scan technology. The following applies to one-to-one verification as opposed to identification, discussed below. The sample capture will generally consist of a 20-30 second enrollment process whereby several pictures are taken of one's face. Ideally, the series of pictures will incorporate slightly different angles and facial expressions, to allow for more accurate searches. After enrollment, distinctive features - corresponding to the four primary types of face technology discussed below - are extracted, resulting in the creation of a template. The template is much smaller than the image from which it is drawn. Whereas quality facial images generally require 150-300kb, the templates are approximately 1300 bytes, or less than 1/100th the size of the original.

Authentication follows the same protocol. Assuming your audience is a cooperative audience (as opposed to uncooperative or non-cooperative), the user claims an identity such as a login name or a PIN, stands or sits in front of the camera for a few seconds, and is either verified or rejected. This comparison is based on the similarity of the newly created, "live" template against the template or templates on file. The degree of similarity required for verification, also known as the threshold, can be adjusted for different personnel, PC's, time of day, and other factors.

One variant of this process is the use of facial scan technology in forensics. Biometric templates taken from static photographs (mug shots) of known criminals are stored in large databases. These records are searched, 1-to-many, to determine if the detainee is using an alias when being booked.

5.1.3. Verification vs. Identification

The system designs for facial scan verification vs. identification differ in a number of ways. The primary difference is that identification does not require a claimed identity. Instead of employing a PIN or user name, then delivering confirmation or denial of the claim, identification systems attempt to answer the question "Who am I?" If there are only a handful of enrollees in the database, this requirement is not terribly demanding; as databases grow very large, into the tens and hundreds of thousands, this task becomes much more difficult. The system may only be able to narrow the database to a number of likely candidates, then require human intervention at the final verification stages.

A second variable in identification is the dynamic between the target subjects and capture device. In verification, one assumes a cooperative audience, one comprised of subjects who are motivated to use the system correctly. Facial scan systems, depending on the

exact type of implementation, may also have to be optimized for non-cooperative and uncooperative subjects. Non-cooperative subjects are unaware that a biometric system is in place, or don't care, and make no effort to either be recognized or to avoid recognition. Uncooperative subjects actively avoid recognition, and may use disguises or take evasive measures. Facial scan technologies are much more capable of identifying cooperative subjects, and are almost entirely incapable of identifying uncooperative subjects.

FACE RECOGNITION AT A GLANCE

Accurate	Accuracy rivals best fingerprint systems (AFIS).
Cost Effective	Pure software that uses low cost, off-the-shelf hardware.
Passive	Requires no user participation.
High User Acceptance	Non-invasive, simple and hands-free.
Human-Readable Audit Trail	Keeps a time/date stamped facial image of every event.
Often Only Suitable Biometric	For applications such as combating ID fraud, continuous monitoring for information security, surveillance and other law enforcement programs.
Uses Existing Databases	Can work from existing images, does not require new enrollment.
Human Backup	Humans are adept at recognizing faces and hence, in case of system downtime, a human can be used as a backup.

5.1.4. Comparing the Primary Facial Scan Technologies:

The four primary methods employed by facial scan vendors to identify and verify subjects include

eigenfaces

feature analysis

neural network

automatic face processing

Some types of facial scan technology are more suitable than others for applications such as forensics, network access, and surveillance.

"Eigenface," roughly translated as "one's own face," is a technology patented at MIT which utilizes two dimensional, global grayscale images representing distinctive characteristics of a facial image. Variations of eigenface are frequently used as the basis of

As suggested by the graphic, distinctive characteristics of the entire face are highlighted for use in future authentication. The vast majority of faces can be reconstructed by combining features of approximately 100-125 eigenfaces. Upon enrollment, the subject's eigenface is mapped to a series of numbers (coefficients). For 1-to-1 authentication, in which the image is being used to verify a claimed identity, one's "live" template is compared against the enrolled template to determine coefficient variation. The degree of variance from the template, of course, will determine acceptance or rejection. For 1-to-many identification, the same principle applies, but with a much larger comparison set. Like all facial recognition technology, eigenface technology is best utilized in well-lit, frontal image capture situations.

Feature analysis is perhaps the most widely utilized facial recognition technology. This technology is related to Eigenface, but is more capable of accommodating changes in appearance or facial aspect (smiling vs. frowning, for example). Visionics, a prominent facial recognition company, uses Local Feature Analysis (LFA), which can be summarized as an "irreducible set of building elements." LFA utilizes dozens of features from different regions of the face, and also incorporates the relative location of these features. The extracted (very small) features are building blocks, and both the type of blocks and their arrangement are used to identify/verify. It anticipates that the slight movement of a feature located near one's mouth will be accompanied by relatively similar movement of adjacent features. Since feature analysis is not a global representation of the face, it can accommodate angles up to approximately 25° in the horizontal plane, and approximately 15° in the vertical plane. Of course, a straight-ahead video image from

a distance of three feet will be the most accurate. Feature analysis is robust enough to perform 1-1 or 1-many searches.

In Neural Network Mapping technology, favored by Miros, features from both faces - the enrollment and verification face - vote on whether there is a match. Neural networks employ an algorithm to determine the similarity of the unique global features of live versus enrolled or reference faces, using as much of the facial image as possible. An incorrect vote, i.e. a false match, prompts the matching algorithm to modify the weight it gives to certain facial features. This method, theoretically, leads to an increased ability to identify faces in difficult conditions. As with all

primary technologies, neural network facial recognition can do 1-1 or 1-many.

Automatic Face Processing (AFP) is a more rudimentary technology, using distances and distance ratios between easily acquired features such as eyes, end of nose, and corners of mouth. Though overall not as robust as eigenfaces, feature analysis, or neural network, AFP may be more effective in dimly lit, frontal image capture situations.

5.1.5. Applications

Identification (one-to-many searching): To determine someone's identity in identification mode, Face recognition system quickly computes the degree of overlap between the live face print and those associated with known individuals stored in a database of facial images. It can return a list of possible individuals ordered in diminishing score (yielding resembling images), or it can simply return the identity of the subject (the top match) and an associated

Confidence level.

Verification (one-to-one matching): In verification mode, the faceprint can be stored on a smart card or in a computerized record. Face recognition system simply matches the live print to the stored one--if the confidence score exceeds a certain threshold, then the match is successful and identity is verified. One

application include Customer ID verification at ATMs, kiosks and PC networks.

Monitoring: Using face detection and face recognition capabilities, Face recognition system can follow the presence and position of a person in the field of view.

Surveillance: Face recognition system can find human faces anywhere in the field of view and at any distance, and it can continuously track them and crop them out of the scene, matching the face against a watch list. Totally hands off, continuously and in real-time. The surveillance market is a perfect fit to the human face biometric. No other biometric can provide the passive, non-intrusive and cost effective performance that face does for the surveillance market. Examples of customers within this market include security departments of casinos, international airports, military bases, retail point-of-sale and government buildings. These security customers have the need to identify known deviants or terrorists before trouble occurs.

Limited size storage devices: Face recognition system can compress a face print to as low as 84 bytes for use in smart cards, bar codes and other limited size storage devices.

ID Solutions: Identity fraud involved identity theft, duplicate aliases and fictitious identities. Identity fraud in any form starts when an individual receives multiple ID documents (driver's licenses, passports, visas, national ID's, etc) under assumed identities.

This is possible because in most countries the so-called breeder documents such as birth certificates are very easy to fake. (In the US alone, there are nearly 10,000 different forms of acceptable birth certificates.) Databases may contain facial photographs - the information required to prevent duplication. However, in practice, it is impossible for a human to search over millions of photos to find those duplicates.

Face recognition system eliminates identity fraud at is source by checking for duplicates and aliases, quickly, reliably and automatically. It is the only biometric solution that can be human operator and that returns a result in real-time.

Criminal Investigation: Often times, law enforcement officials have no more than a facial image to link a suspect to a particular crime or previous event. Up to now, database searches were limited to textual entries (i.e., name, social security number, birth date, etc.) leaving room for error and oversight.

By conducting searches against facial images, Face recognition systems yields instant results, verifying the identity of a suspect instantly and checking through millions of records for possible matches quickly, automatically and reliably.

No other technology gives law enforcement the ability to identify suspects without their active participation. No other technology is as widely used by local law enforcement and in secure internet booking systems.

Border control is other useful application for face recognition systems.

Online authentication: Our service provides the most secure and easiest-to-use method to verify user IDs with the least amount of capital outlay, integration effort, administration, maintenance and risk. Applications include:

Online banking and accounting.
Online stock trading
Business-to-business brokering of valuable products on the Web
Online financial services
Online insurance service
Online verification for e-commerce and home workers

How can hospitals be sure that only authorized people can access patient records or prescribe drugs online? Passwords alone are inadequate.

Online learning and testing is growing quickly. Using passwords to determine the identity of the user will not prevent someone else from taking the test.

5.2. Voice recognition

The allure of talking to your computer. Everyone who has watched StarTrec (Next Generation) has whitened Picard ordering tea from the computer -- “Computer.. Tea.. Earl Gray.. Hot..”. Imagine drawing in CADD by saying “line from end of.. (pick) to middle of (pick). Sounds a bit too far out in space to be real? Well the “far out” is closer than you might think. IN3 software by Command Corp. Inc allows the AutoCAD users to define 20 voice commands (demo version). Before a command can be recognized, a corresponding voice template must first be created. A voice template represents energy associated with the way in which you pronounce the command. Naturally there are variations in the way you say a command each time it is spoken. The template refining process builds on the base voice template to create a refined template that represents your normal range of pronunciations.

Pre-programmed AutoCAD commands in the demo version include: microphone (on/off for IN3); cancel; centre; circle; endpoint; erase window; intersect; midpoint; nearest; ortho; perpendicular; pline; quadrant; redo; tangent; undo; zoom all; zoom extents; zoom previous; zoom window. Training the program to recognize your voice is a simple (if not mindless) process. By the time I was to the “UNDO” command, I was becoming bored/creative. What if I uttered a different word to define UNDO... Say “Dough” Homer Simpson's favorite saying for making a mistake (Yet more references to TV genera).

So now I had my voice template completed! Off to try out AutoCAD. Unfortunately the voice recognition software only recognizes the commands exactly the way they are input. If their is a subtle variation in how you pronounce the command, it will not be recognized. Imagine trying to get an UNDO command by saying “DOUGH” “dOUgh” “DOUgh” (emphasis placed on the capital letters). I found that voice recognition correctly identified the CADD commands 80% of the time. I was constantly watching the command prompt to verify that IN3 had infant “got” the correct response. Now imagine an office full of CADD users all saying “DOUGH” ““dOUgh” “DOUgh”. What a strange chorus. And even harder to explain to upper management.
So for the near future, the use of Mice/digitizers and key boards looks secured. Some day voice recognition will be 100% till then -- “beam me up Scottie”.

IN3 Hardware requirements:

Generally, all 486 based PCs have enough processing power to perform IN3 recognition with at least 100 commands active for recognition while running user-interactive applications. A high quality head-worn microphone provides high performance in production environments like CAD and publishing. IN3 Voice Command runs with most 8 and 16 bit Windows compatible audio boards. In selecting an audio board for use with IN3, choose a board that records and plays back audio that is relatively free and hum.]

MD Dictate is a software program that turns your spoken words into detailed documentation.

By dictating into a customized speech microphone, which is plugged into an MD Dictate equipped personal computer, your words will instantly appear as digital documentation. Transcription costs, errors and delays are completely eliminated.

But the advantages don't stop with simply converting your voice into printed words … MD Dictate also has the ability to create an unlimited number of customized templates and macros. Many health care providers have some form of common or repetitive documentation. An MD Dictate template allows physicians to
simply "talk" their way through that repetitive documentation, changing only the patient variables. With MD Dictate templates, a five-minute dictation can be reduced to 5 seconds. MD Dictate macros are used where large blocks of text are employed to describe a patient's "normal" condition. By identifying each of these normal macros with a specific phrase, reams of supporting documentation can be added to a
patient's file with a single spoken word.

5.3. Finger prints recognition:

In North America, one of the first successful uses of fingerprints for identification was by e. Henry in 1901 to stop the railway workers from double collecting pay.

The Henry system derives from the pattern of ridges; concentrically patterning the hands, toes, feet and in this case, fingers.

The classic method of inking and rolling fingers on a print card produces a pattern unique to each individual digit.

According to the bundeskriminalamt (BKA); no two individuals have identical ridge patterns, ridge patterns are not inheritable, ridge patterns are formed in the embryo, ridge patterns never change in life, and after death may only change as a result of decomposition. in life, ridge patterns are only changed by accident, injury, burns, disease or other unnatural causes.

Identification from fingerprints requires the differentiation of uninterrupted papillary ridge contours followed by the mapping of anatomic marks or interruptions of the same ridges.

Papillary ridge patterns: loop, arch, whorl, tented arch, double loop, central pocket loop and accidental.

The most common three are illustrated below. (The `loop´ has 1 delta and the lines between the center of the loop and the delta must show. the `whorl' has 2 deltas and the lines between the deltas must be clear. the `arch' has no deltas.)

All the above patterns can be discerned by the naked eye and can give a binning or indexing of the resulting databases. the computer can by vector analysis of the change of direction of the ridge lines, achieve what the trained eye naturally sees.
Errors can occur if this step is omitted by a computer finger print program or AFIS (automatic fingerprint identification).

Anatomic characteristics occur because the papillary ridges are not continuous. Each change of direction, bifurcation, interruption or enclosure produces anatomic characteristics (minutia in law enforcement). These characteristics may not be readily available to the human eye but are easily tracked by the computer.

The BKA gives the following as their anatomic characteristics criteria.

	ridge	defined as having double the distance from starting to ending, as neighboring ridges are wide
	evading ends	two ridges with different directions run parallel with each other for more than 3mm.
	bifurcation	a ridge splits, both ridges maintain the same direction and are longer than 3mm
	hook	a ridge splits; one ridge is not longer than 3mm
	fork	two ridges are connected by a third ridge not longer than 3mm
	dot	the ridge section is no longer than the neighboring ridges are wide
	eye	the ridge splits and rejoins within 3mm
	island	a ridge splits and joins again within not less than 3mm and not more than 6mm. The enclosed area is ridgeless.
	enclosed ridge	a ridge not longer than 6mm between two other ridges
	enclosed loop	a non-pattern determining loop between two or more parallel ridges.
	specialties	rare ridge forms such as question marks and butcher hooks

The use of features or minutia is limited to the area the sensor collects as opposed to the entire fingerprint collected by inking and rolling.

This features are further reduced in contact sensors. Increased pressure will only flatten so much of the fingertip to contact the sensor area. Also more pressure equates to more distortion.

The papillary ridge area is sometimes referred to as the pattern area. Each papillary ridge pattern produces a different pattern area shape.
The center of the finger image, reflecting the pattern area; is referred to as the core point. This allows for registering the image to compensate for aspect changes caused by uncooperative users giving side images for example.

The two parallel inner ridges that diverge to surround the pattern area are called type lines. They may not be continuous and due to the limitation of some sensors, they also may appear fragmented.

The point of initial bifurcation, or other anatomic feature at the point of divergence of two type lines, is called the delta. Usually it is placed directly in front of the type line bifurcation.

Now by drawing a line from the delta to the core point, the number of ridge intersections within the pattern area gives a ridge count.

Computer tomography can augment the above with the following either relative to above mentioned points or independently in x-y space.

The anatomic characteristics have an orientation or direction. a vector analysis of the direction change of the ridge lines can produce an average that reflects this orientation .

The distance between ridge lines and anatomic feature give a length to the vector produced by orientating the anatomic characteristics. this is dependent on the sensor reproducing repeatable results independent of pressure spread or melting of the ridgelines.
The resultant orientation and vectors can be overlaid in x-y to give a template.

Templates produced from anatomic features independent of pattern and curvature can be erroneous as different patterns can have the same anatomic characteristic points. No two fingerprints are identical because of pattern and the number of the anatomic characteristics but anatomic characteristics alone are too small subset to rely on.

6. CONCLUSION:

Security technology in its different areas which is under research takes few more years to get its complete structure and come into utilization. Since this technology requires powerful hardware devices and software environments, it is doubtful that the common man can afford them.