BIOMETRICS
Biometrics are automated methods of recognizing a person based on a physiological or behavioral characteristic. It is the science and technology of measuring and statistically analyzing biological data. In information technology, biometrics usually refers to technologies for measuring and analyzing human body characteristics such as fingerprints, eye retinas and irises, voice patterns, facial patterns, and hand measurements, especially for authentication purposes.
Often seen in science-fiction action adventure movies, face pattern matches and body scanners seem about to emerge as replacements for computer passwords.
BIOMETRIC VERIFICATION
Biometric verification is any means by which a person can be uniquely identified by evaluating one or more distinguishing biological traits. Unique identifiers include fingerprints, hand geometry, earlobe geometry, retina and iris patterns, voice waves, DNA, and signatures. The oldest form of biometric verification is fingerprinting. Historians have found examples of thumbprints being used as a means of unique identification on clay seals in ancient China. Biometric verification has advanced considerably with the advent of computerized databases and the digitization of analog data, allowing for almost instantaneous personal identification.
In all automated systems, the fundamental operational steps are:
Capture: The biometric data is captured, digitized and entered into a database.
Extraction: A template is created using this measurable unique data.
Comparison: The template is compared with a new sample.
Match/Non-Match: The existing template matches the new sample or it does not.
The goal of most automated biometric ID systems is one of two outcomes:
Verification: Is the person who they claim to be?
Identification or recognition: Who is this? Is the person already known to the system under a different identity?
TECHNIQUES AND TECHNOLOGIES USED FOR BIOMETRIC VERIFICATION
- Fingerprint verification.
- Hand-geometry.
- Face-recognition.
- Signature dynamics.
- Voice dynamics.
- Retinal scan.
- Iris scan.
- Vein biometric.
- DNA analysis.
FINGERPRINT TECHNOLOGY
This is perhaps the oldest known identification technique that is still used on a large scale. Common examples are criminal records all over the world, forensic and access-control mechanisms in high-security establishments. Relatively simple to deploy and easier to use than other biometric techniques, fingerprint identification is based on the uniqueness of one’s fingerprint. The principle is simple enough and involves a high-resolution snapshot of the fingerprint taken and stored in a database. A wide variety of sensors are available to capture fingerprints. The most commonly used are optical sensors. Some vendors use sound wave sensors or electrical capacitance to identify fingerprint minutiae features. Another uses radio frequency (RF) sensors. Two approaches are followed for getting the final match: minutiae-based recognition and correlation-based approach. While the first relies entirely on mapping the complete print, the second works on the gray-scale information of the print.
Minutiae Based:
Look at an enlarged image of a fingerprint and you will find that it is made up of a series of ridges and furrows. These run parallel to each other and also form whorls, arches and loops. Minutiae points are characteristics of these ridges at endings and bifurcations. These points can be identified in a sample of a fingerprint. These are then matched against a database image. Depending upon a threshold number of minutiae points, the decision on a ‘match’ can be taken. The accuracy of this approach, however, depends on some factors. First is the quality of the fingerprints. Smudged prints and prints with a lot of ‘noise’ restrict the number of minutiae that can be uniquely identified. Also, identification points (minutiae) are not representative of a complete fingerprint.
Correlation based:
Unlike the previous approach, the correlation method is more representative of the complete print. The identification system identifies ‘templates’ in the print. These templates are gray-scale data. A scheme of template matching is then used to identify matching positions in the print to be verified. Template data from these matching positions is then compared. The limitations of this scheme are basically because of orientation and location of the vertex for the template, which determines the directional gray-scale data. This will in turn determine that the identification hardware or software is ‘looking’ at the same orientation in the stored print and the print to be identified. Developments in technology and methods of capturing fingerprints have improved the reliability of fingerprint-recognition systems. So we have small, pointable devices to capture prints and huge databases that can be searched within seconds to authenticate people. This will ensure that this field of biometrics remains popular. Finger imaging is usually associated with Automated Finger Image Systems (AFIS) used worldwide by law enforcement agencies. In these systems, enrollment records are made up of data from all ten fingers, thus they are referred to as "10 finger" systems.
Popular Applications
- AFIS - Police/FBI/Interpol
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- Welfare ID Systems
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HAND GEOMETRY
Hand geometry involves analyzing and measuring the shape of the hand. This biometric offers a good balance of performance characteristics and is relatively easy to use. It might be suitable where there are more users or where users access the system infrequently and are perhaps less disciplined in their approach to the system.
Accuracy can be very high if desired, and flexible performance tuning and configuration can accommodate a wide range of applications. Organizations are using hand geometry readers in various scenarios, including time and attendance recording, where they have proved extremely popular. Ease of integration into other systems and processes, coupled with ease of use, makes hand geometry an obvious first step for many biometric projects.
SIGNATURE DYNAMICS
Signature dynamics involves breaking-down of the way a signature is made. Some qualities looked into include acceleration rates, directions, pressure, and stroke length. A flaw with this technology lies in the randomness or the inconsistency with which a user makes his signature. Due to this, multiple matches might result in a higher number of FARs or FRRs. Accuracy is thus below satisfactory levels and so can currently only be implemented in areas where precision is not the only relevant factor in decision-making.
VOICE DYNAMICS
When considering voice recognition it is first important to differentiate between speech recognition and voice recognition. Speech recognition recognizes "what is being said." Examples of speech recognition can be found everywhere in modern society . You use them everyday in services ranging from call center operations to operator assistance calling services used by your local telephone company.Voice recognition is entirely different and focuses on recognition of the individual. It is language independent and measures physiological characteristics of the speakers:
- Vocal chords.
- Trachea.
- Resonance of the nasal passages .
- How the tongue moves in your mouth to help create certain sounds.
Voice samples can be captured with a microphone, a telephone or other similar means. Depending on the length of the sample captured, a voice print could take up as little as 20-40kb. Some voice verification experts maintain that even identical twins cannot produce an exact voice print match.
Popular Applications:
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FACE RECOGNITION
The human ability to look at a face, memorize it, and recall it when you meet the same person again is entering the domain of the computer. Called facial recognition, this technique enables the computer to use your face as your password, or identify criminals by looking at photographs of a crowd and matching these to an existing database. The advantages of face recognition over other biometric techniques are that it’s non-intrusive and less expensive to set up. The subject of recognition needn’t click on anything or give his fingerprint, for instance, his photograph can be captured even without his knowledge. It’s less expensive in the sense that databases of employees, national citizens, criminals, etc. may already exist. The hardware required for a small home or office setup is also not very expensive all you need are standard video cameras with a resolution of at least 320x240 and a frame rate of at least 3–5 fps, a good video card, and a processor with enough speed. After this, all you need to buy is the software.
Like other biometric techniques, recognizing a face involves taking pictures of that face, extracting its features, creating a template from these features, and comparing this to existing templates in a database. Two techniques ‘eigenfaces’ and ‘local feature analysis’ re primarily used
Eigen faces:
Also called PCA (Principle Component Analysis), this technology is patented at MIT, and is being used by Viisage in its face-recognition software. Pictures of millions of faces are used to build the eigenface database. These pictures are first compared to see if any features are common between them, and are then used to build the database, which comprises two-dimensional, grayscale, global (taking the face as a single entity) images, each of which has different light and dark areas to highlight different characteristics of a face. The premise is that any face can be built by combining 100–125 eigenfaces. To begin facial recognition, a photograph of the subject is used to to create a ‘template’, by encoding the face’s characteristics into numbers. This template is compared to the eigenfaces’ database, which throws up results the faces which the template matches most closely. The disadvantage of eigenfaces is that since it considers global images, a change in facial expression of the subject smiling or frowning, or a change in pose or lighting skews the results. LFA (local feature analysis) does away with this weakness.
Local feature analysis:
Currently used by Visionics’ face-recognition software, FaceIt, LFA takes a face’s features to be its building blocks. It bases its results on individual features and their relative distances from each other. So, if one feature changes, as in the case of a smile, features around it will also change, and LFA will be able to consider this change. FaceIt, for example, identifies 80 nodal points—like the distance between eyes, depth of eye sockets, cheekbones, jaw line or chin—that define a face. These nodal points are used to create a faceprint, a numerical code for that particular face. Each faceprint is compressed to 84 bytes in size and stored. This is then compared to faceprints stored in the existing database to come up with matching results. Other techniques used for facial recognition include neural networks and AFP (Automatic Face Processing) but these haven’t been implemented widely yet. Face recognition can be used widely, from authenticating people for access to computer networks, bank ATMs, etc, through smart cards, to searching for terrorists or criminals in crowded places (Face It was recently used for this on spectators at a football match in Tampa); for surveillance or on voters’ ID cards or passports to prevent fraud. Taking this to the extreme, however, may soon lead to a day when ‘Big Brother is watching you’ becomes an everyday reality.
IRIS RECOGNITION
The iris has colored streaks and lines that radiate out from the pupil of the eye. The iris provides the most comprehensive biometric data after DNA. And the chances that any two people may have the same pattern is one in 10 to-the-power-78, which is way above the current population of the Earth. In this scanning, the characteristics of the iris are taken into account. Iris scanning can be done at day or night, with glasses or contact lenses on. Since iris scanning can be done from up to 2 feet away, it is not considered intrusive. Iris patterns are complex and unique. In 1985 Drs. Leonard Flom and Aran Safir proposed the concept that no two irises are alike. In 1994, Dr. John D. Daugman developed the sophisticated mathematical foundations that made automated iris recognition into reality. Zones of analysis are established. Even pupil dilation is accounted for. A monochrome CCD camera is used to capture an image of the iris. That captured image is turned into an enrollment template and then encrypted (512 byte iris code). This template is then used for comparison.
Popular applications include
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RETINAL-RECOGNITION
Like fingerprints, the retina and iris of the human eye exhibit uniqueness for each human. The retina is an internal part of the eye, while the iris is the outer colored part. The retina is located at the back of the eye, and is a set of thin nerves which senses the light coming through the cornea, pupil, eye lens and vitreous humor, in that order. The pattern of blood vessels which make up the retina are unique for each individual. The unique pattern of the blood vessels can recorded by a retina scan device. The individual whose retina pattern has to be scanned, must have his eye located at a distance of not more than a half inch. Also the position of the eye must not move while it is scanned. While scanning the individual looks at a rotating green light. For recognizing the patterns about 400 unique points on the blood vessels are recorded. For authentication, the recorded pattern is compared against the blood vessel pattern of the individual. If they match, access denied else prohibited.
VEIN-RECOGNITION
According to Joseph Rice, an expert on this technology, "...Vein biometric systems record Infra Red (IR) absorption patterns to produce unique and private identification templates for users. Veins and other features present large, robust, stable and largely hidden patterns. Subcutaneous features can be conveniently imaged within the wrist, palm, and dorsal surfaces of the hand. Vein pattern IR grey-scale images are binarized, compressed and stored within a relational database of 2D vein images. Subjects are verified against a reference template. The technology can be applied to small personal biometric systems e.g. Biowatches and Biokeys and to generic biometric applications including intelligent door handles, door locks etc".
DNA ANALYSIS
DNA analysis, which involves checking the DNA patterns of a human, is used when the physical characteristics are unrecognizable, especially to identify dead people. It is also used to find out relationships, like in cases involving identifying a child’s natural parents. This is one biometric technology that is judicially accepted. Since no humans have identical DNA patterns (with the rare exception of twins), this is one of the most foolproof methods of all. Modern technology allows the system to scan the DNA directly by taking dead cells from a person’s external skin.
CONCLUSION
Hence we have seen the basic details of biometric and how is useful to human being and advancement in routine life of society.
EDITOR
LAMDANDE KIRAN G.
MASTER OF TECHNOLOGY IN INSTRUMENTATION
(COMPUTERIZED PROCESS CONTROL)
MOBILE NO:- +919970606673
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