Post a New Question | Current Questions | Live Experts
Homework Help: Science: Biology: Biometrics
by Soumyadip Rakshit
Abstract: In the modern world, there is an ever-increasing need to authenticate and identify individuals automatically. Securing personal privacy and deterring identity theft are national priorities. Biometrics, the physical traits and behavioural characteristics that make each of us unique, are a natural choice for identity verification. It is an emerging technology that promises an effective solution to our security needs. It can accurately identify or verify individuals based upon their unique physical or behavioural characteristics. It is a key that can be customized to an individual’s access needs opening doors for one while keeping others out. We can use a biometric to access our home, our account, or to invoke a customized setting for any secure area or application. In this paper we explore the various types of biometric authentication techniques and their deployment potential. We take a look into the emerging technologies in this field and note their potential applications and future prospects.
Introduction: The art and science of biometrics is all about coming up with an all-purpose personal identifier. Automated methods of recognizing a person based on a physiological or behavioural characteristic is the basic fact underlying it. Biometric authentication is the "automatic", "real-time", "non-forensic" subset of the broader field of human identification. Humans recognize each other according to their various characteristics. For example, we recognize others by their face when we meet them and by their voice as we speak to them. Identity verification in computer systems has traditionally been based on something that one has or one knows. These however tend to get lost or stolen. To achieve more reliable verification or identification something that really characterizes the given person should be used for this purpose.
A biometric system is essentially a pattern recognition system that establishes a person’s identity by comparing the binary code of a uniquely specific biological or physical characteristic to the binary code of the stored characteristic. This is accomplished by acquiring a live sample from an individual who is requesting access. The system then applies a complex and specialized algorithm to the live sample and converts it into a binary code and then compares it to the reference sample to determine the individual's access status. A profile or template containing the biometrical properties of a person is stored in the system (generally after data compression), by recording his characteristics. These characteristics are scanned several times during enrolment in order to get a profile that corresponds most with reality. A scan of the biometrics of a person is made and compared with the characteristics that are stored in the profile.
Classifications: There are various ways to classify biometric systems and devices. Let us take a look at some of them. First, we look at the classification of systems based on their functions. Biometric systems can be used in two different modes:
- Verification, and
- Identification.
Identity verification occurs when the user claims to be already enrolled in the system, and the biometric data obtained from the user is compared to the data stored in the database. Identification, on the other hand, occurs when the identity of the user is a priori unknown. The user’s data is matched against all the records in the database. Identification is technically more challenging and costly. It’s accuracy decreases as the size of the database grows. For this reason records in large databases are categorized according to a sufficiently discriminating characteristic in the biometric data. Subsequent searches for a particular record are searched within a small subset only. This lowers the number of relevant records per search and increases the accuracy.
Now we take a look at the classification of biometric devices. These are classified according to two distinct functions:
- Positive Identification: To prove one is enrolled in the system.
- Negative Identification: To prove one is not enrolled in the system.
These functions are "duals" of each other. In the first function, the present person is linked with an identity previously registered, or enrolled, in the system. The second function, establishes that the present person is not already present in the system. The purpose of this negative identification system is to prevent the use of multiple identities by a single person. If a negative identification system fails to find a match between the submitted sample and all the enrolled templates, an "acceptance" results. A match between the sample and one of the templates results in a "rejection".
Each technology has its strengths and weaknesses depending upon its application. It is therefore imperative that we have a clear understanding of what the final application will be, before we delve any further. Although the use of each biometric is clearly different, some striking similarities emerge when considering applications as a whole. All applications can be partitioned according to the following categories:
- Cooperative or Non-cooperative,
- Overt or Covert,
- Habituated or Non-habituated,
- Attended or Non-Attended,
- Standard Environment or Non-Standard operating environment,
- Public or Private, and
- Open or Closed.
Errors: No biometric system can verify the identity of a person absolutely. It cannot give simple yes/no answers. Instead, it measures how similar the current biometric data is to the record stored in the database, and makes a decision according to the probability that the two biometric samples come from the same person. No biometric system is flawless. There are two kinds of errors present in any biometric system:
- False rejection: A legitimate user is rejected because the system does not find his current biometric data similar enough to the master template stored in the database.
- False acceptance: An impostor is accepted as a legitimate user because the system finds the impostor’s biometric data similar enough to the master template of a legitimate user.
If this margin is too small, the system will reject a righteous person (false-rejection), while if this margin is too large, malicious persons will be accepted by the system (false-acceptance).
Properties: Any human physiological or behavioural characteristics can become a biometric provided the following properties are fulfilled:
- Universality: Every person should have the characteristic. There are mute people, people without fingers or with injured eyes; all of these must be taken into account.
- Uniqueness: No two persons should have the same biometric characteristics. Identical twins cannot be easily distinguished by face recognition and DNA-analysis systems.
- Permanence: The characteristics should be invariant with time. A person’s face changes significantly with time and the signature and its dynamics may change as well.
- Collectability: Obtaining the characteristics should be easy. DNA analysis requires a blood or other bodily sample. The retina scan is rather intrusive as well.
- Performance: It is the accuracy, resources and environmental conditions required to achieve the desired results. The accuracy of some signature dynamics systems is as low as 75% and the verification decision takes over one second.
- Circumvention: This refers to how difficult it is to fool the system by fraudulent techniques. An automated access control system that can be easily fooled with a fingerprint model or a picture of a user’s face does not provide much security.
- Acceptability: This indicates to what extent people are willing to accept the biometric system. Face recognition systems are personally not intrusive, but there are countries where taking pictures of persons is not viable. The retina scanner requires an infrared laser beam directed through the cornea of the eye. This is rather invasive and only few users accept this technology.
Characteristics: There are two major categories of biometric technologies according to what they measure:
Behavioural characteristics
- Keystroke dynamics,
- Voice,
- Gait, and
- Signature dynamics.
Physical characteristics
- Fingerprint,
- Face,
- Retina,
- Iris,
- Vein pattern, and
- Hand and finger geometry.
We now look into each of the aforementioned characteristics and delve into their underlying technology and applications.
Keystroke dynamics: Keystroke dynamics, also referred to as typing rhythms, is one of the most unusual and innovative biometric technologies in use today. It is an automated method of examining an individual’s keystrokes on a keyboard. This technology examines such dynamics as speed, pressure, total time taken to type particular words, and the time elapsed between hitting certain keys. Specifically, keyboard dynamics measures two distinct variables: "Dwell time" which is the amount of time we hold down a particular key and "Flight time" which is the amount of time it takes in between keys. Keyboard dynamics systems can measure one's keyboard input up to 1000 times per second. This technique can be classified as either static or continuous. Static verification approaches analyse keystroke verification characteristics only at specific times, for example, during the login sequence. They provide more robust user verification than simple passwords, but do not provide continuous security. They cannot detect a substitution of the user after the initial verification. Continuous verification, on the contrary, monitors the user's typing behaviour throughout the course of the interaction.
The algorithms are still being developed to improve robustness and distinctiveness. It works well with users that can "touch type". The main advantage in applying keystroke dynamics is that the device used in this system, the keyboard, is unobtrusive and does not detract from one's work. Enrolment as well as identification goes undetected by the user. Another inherent benefit to using keystroke dynamics is that the hardware is inexpensive. Currently, keystroke dynamics has technical issues that must be addressed before it becomes widespread. One such issue is the standardization among computer keyboards and the lack thereof. Standards among keyboards must be resolved and communication protocol structures put in place before keystroke dynamics can successfully enter the marketplace.
Keystroke dynamics has many applications in the computer security arena, like restricting root level access to the master server hosting a key database. Any user trying to access the server is prompted to type a pass phrase along with his username and password. Access is granted if his typing pattern matches within a reasonable threshold with that of the claimed identity. Dynamic or continuous monitoring of the interaction of users while accessing highly restricted documents or executing tasks in environments where the user must be alert at all times is a ideal scenario for the application of a keystroke authentication system. For example keystroke dynamics may be used to detect uncharacteristic typing rhythm, brought on by drowsiness, fatigue etc. in air traffic controllers and notify third parties for necessary action.
Voice: Voice identification is a hybrid between behavioural and physiological biometric. A voice identification system, like other biometric technologies, requires that a "voice reference template" be constructed so that it can be compared against subsequent voice identifications. To construct the "reference template" an individual must speak a given pass phrase several times as the system builds the template based on numerous characteristics, including: cadence, pitch, tone, shape of larynx, dynamics, and waveform. There are five specific forms of voice identification technologies that are currently available or under development:
- Speaker Dependent,
- Speaker Independent,
- Discrete Speech Input,
- Continuous Speech Input, and
- Natural Speech Input.
A major concern for voice identification systems is how to account for the variations in one's voice each time identification occurs. The rate and pitch at which an individual speaks is not always the same. To help eliminate these types of variations during voice identification, a process comprising Hidden Markov Modelling is applied. The basis of this approach is that the system uses language models to determine how many different words are likely to follow a particular word.
Most applications of voice identification today fall under the industries of call-answering and contact-management services. Other markets that voice verification has penetrated recently include medium-security access control and time and attendance monitoring. Some designs have wall-mounted readers whilst others integrate voice verification into conventional telephone handsets. Speaker verification works with a microphone or with a regular telephone handset, although performance increases with higher quality capture devices.
There are many advantages to using voice identification. It provides eyes and hands-free operation, is reliable, flexible, and has a good data accuracy rate. Voice identification technology continues to grow and improve. In the future, voice identification will not only be used for text dictation but to run applications and control predetermined commands. It has also been estimated that if voice identification technology continues to progress as it has, keyboards will become obsolete in a decade.
Gait: Gait recognition can be used to monitor people without the need for their cooperation. It can spot people who are moving around in suspicious ways, which may include repetitive walking patterns or movements that don't appear natural given their physicality. It could also be used in conjunction with other biometric systems to verify people's identities and weed out impostors. Though still in its infancy, the technology is growing in significance. At present, gait recognition is much less diagnostic than other methods, but it can act as a filter and a screening tool in conjunction with other biometric methods.
Gait recognition can be achieved by computer vision or with the help of a radar system. The former uses visually-based systems that use video cameras to analyse the movements of each body part - the knee, foot, shoulder, and so on, while the latter uses a radar-based system. As an individual walks towards the system, they're bombarded with invisible radio waves. Each individual's walking speed and style will make those waves bounce back a little differently. The result is a kind of composite signature that characterizes the overall feel of their walk. Computer analysis can be used to parse digital video images and study both static body and stride parameters. Data such as distances between head and foot, head and pelvis, foot and pelvis and left foot and right foot can be measured.
The ultimate goal is to detect, classify and identify humans at large distances under day or night, all-weather conditions. As distance increases, face recognition becomes a problem. Gait recognition can be used to screen people at sensitive places such as airports and military establishments. An airport tarmac is a classic example of where this technology could be used. It is a place to which only a limited number of people have access. These new identification methods hold promise as tools in the war on terrorism and in medical diagnosis. Basic changes in someone's walking pattern can be an early indicator of the onset of Parkinson's disease, multiple sclerosis and normal pressure hydrocephalus (NPH).
Signature dynamics: Signature identification, also known as Dynamic Signature Verification (DSV), analyses the way a user signs his name. It analyses two different areas of an individual's signature: the specific features of the signature and specific features of the process of signing like speed, pen pressure, directions, stroke length, and the points in time when the pen is lifted from the paper. Signature identification devices can also analyse the "static" image of one's signature. In this case the device captures the image of one's signature and saves it for future comparisons to the stored template. To account for the change in one's signature over time, signature identification systems adapt to any slight variances over time. The way a dynamic signature identification system accomplishes this is by recording the time, history of pressure, velocity, location and acceleration of a pen each time a person uses the system.
Signature verification enjoys a synergy with existing processes that other biometrics do not. It is a natural fit in the world of biometrics since identification through one's signature occurs during everyday transactions. People are used to signatures as a means of transaction-related identity verification, and most would see nothing unusual in extending this to encompass biometrics. The major technological hurdle for signature identification involves the method of trying to differentiate between the parts of the signature that are habitual (consistent) and those that alter with each signing (behavioural). With a good amount of practice, a person might be able to duplicate the visual image of someone else's signature but it is difficult if not impossible to duplicate "how" that person signs their name. The healthcare industry is aggressively adopting signature identification for the submission of new drug applications.
Fingerprint: Fingerprint identification is perhaps the oldest of all the biometric techniques. The skin on the inside of a finger is covered with a pattern of ridges and valleys. Every individual is believed to have unique fingerprints. The papillary ridges in the fingerprint pattern are not continuous lines but lines that end, bifurcate, or form an island. These special points are called minutiae and, although in general a fingerprint contains about a hundred minutiae, for a positive identification 12 minutiae are all that have to be identified, according to the 12-point rule. The fingerprint scanners are based on a variety of techniques such as:
- Optical sensors with CCD or CMOS cameras,
- Ultrasonic sensors,
- Solid state electric field sensors,
- Solid state capacitive sensors, and
- Solid state temperature sensors.
Fingerprint scanners currently cannot make the distinction between a real, living finger and a dummy created from silicone rubber or any other material. Comparing all biometric verification possibilities, fingerprint scanners are perhaps the one of the least secure means of verification. It is the only system where the biometrical characteristic can be stolen without the owner noticing it or reasonably being able to prevent it. But, fingerprint verification is good for in-house systems, where the system operates in a controlled environment and adequate explanation and training can be given to its users.
Face: Face recognition uses the visible physical structure of the face and analyses the spatial geometry of distinguishing features in it to identify an individual. It is accomplished in a five-step process:
- An image of the face is acquired by digitally scanning an existing photograph or by using a camera to acquire a live picture of the subject.
- Software is then employed to detect the location of the face in the acquired image.
- The identifying features of the face are extracted and analysed.
- This template is then compared with those in a database of known faces.
- Depending on the extent of match, a positive or negative result in declared.
The face is registered as a biometric signature after being normalized, so that it is in the same format (size, resolution, view, etc.) as the signatures on the system’s database. A matcher compares the normalized image with the set of normalized signatures on the system’s database. A measure of similarity or difference is computed for each comparison of normalized signatures. Principal component analysis, elastic graph matching, neural networks and distortion-tolerant template matching are some of the techniques used for face recognition. The better the quality of the captured image, the better the system performs.
"Facetraps" are used to acquire high quality images of the target’s face. For example, a surveillance camera can more easily capture images of people at the check-in counter than on a crowded street. A person going up an escalator will naturally look at a flashing red light at the top of the escalator. A surveillance camera located there can easily capture an image; the face has been trapped.
The automatic face recognition involves the resolution of some complex problems like, face localization, invariance to pose and illumination, change in expression, moustache, beard, glasses and hair-style. The development of computational models for the face recognition is a very difficult task. We are only beginning to understand how the human brain recognizes faces. Face recognition has many advantages. The system captures faces of people in public areas, which minimizes legal concerns. It uses legacy databases and can integrate with existing surveillance systems. Moreover, since faces can be captured from some distance away, facial recognition can be done without any physical contact. This feature also gives facial recognition a clandestine or covert capability. The development of a hybrid system to take advantage of more than one single approach is the present task at hand. Facial recognition has been used in projects to monitor card counters, shoplifters in stores, criminals and terrorists in urban areas.
Retina: A retina-based biometric involves analysing the layer of blood vessels situated at the back of the eye. It projects a low-intensity infrared light through an optical coupler to the back of the eye and onto the retina to scan its unique patterns. Infrared light is used due to the fact that the blood vessels on the retina absorb the infrared light faster than surrounding eye tissues. The infrared light with the retinal pattern is reflected back to a video camera. The video camera captures the retinal pattern and converts it into data that is only a few bytes in size. Retinal scanning can be quite accurate but does require the user to look into a receptacle and focus on a given point. The user must be standing very still within inches of the device. This is not particularly convenient if one wears glasses or is concerned about having close contact with the reading device.
Retinal identification has several disadvantages such as:
- Susceptible to disease damage (eg. Cataracts),
- Viewed as intrusive and not user friendly, and
- High amount of user and operator skill required.
However, retinal identification continues to be one of the best biometric performers on the market with low false reject rates, a nearly zero percent false acceptance rate, small data template, and quick identity confirmations.
Iris: The iris is the coloured ring of textured tissue that surrounds the pupil of the eye. Each iris has a unique structure featuring a complex pattern. This can be a combination of specific characteristics known as corona, crypts, filaments, freckles, pits, furrows, striations, and rings. Iris recognition involves analysing features found in the iris using a special grey-scale camera at the distance of 10 - 40 cm from the camera. The iris is an excellent choice for identification: it is formed randomly, stable throughout one’s life, not very susceptible to wear and injury, and it contains a pattern unique to the individual. Indeed, an individual’s right and left iris patterns are completely different, and so are the iris patterns of identical twins.
In the iris, there are over 400 distinguishing characteristics, or Degrees of Freedom (DOF), that can be quantified and used to identify an individual. Approximately 260 of those are used or captured in a "live" iris identification application. These identifiable characteristics include: contraction furrows, striations, pits, collagenous fibers, filaments, crypts (darkened areas on the iris), serpentine vasculature, rings, and freckles. Due to these unique characteristics, the iris has six times more distinct identifiable features than a fingerprint. The iris scanner does not need any special lighting conditions or any special kind of light (unlike the infrared light needed for the retina scanning). If the background is too dark any traditional lighting can be used. Some iris scanners also include a source of light that is automatically turned on when necessary. Iris scanning does not use lasers and requires no close contact between the user and the reader. So there is no health risk at all.
In identifying one's iris, there are two types of methods used by the system: Active and Passive. The former requires the user to move back and forth so that the camera can adjust and focus in on the user's iris. It requires that a user be anywhere from six to 14 inches away from the camera. On the other hand, the latter incorporates a series of cameras that locate and focus on the iris. It allows the user to be anywhere from one to three feet away from the camera(s) and thus provides for a much more user-friendly experience.
Iris identification can be broken down into the following fundamental steps:
- The user stands in front of the system, while a wide-angle camera calculates the position of his eye.
- A second camera zooms in on the eye and takes a black and white image.
- After the system has the iris in focus, it overlays a circular grid on the image of the iris and identifies the areas where light and dark fall.
- The system then recognizes a pattern within the iris and generates 'points' within the pattern into an 'eyeprint'.
- Finally, the captured image or 'eyeprint' is checked against a previously stored 'reference template' in the database and a decision made.
It takes 2 seconds to identify one’s iris. A template iris pattern code contains less than half a kilobyte of data. As a result of this small "electronic footprint", up to 100,000 records a second can be scanned using a standard personal computer. Furthermore, the mismatch rate is as low as one in 1078.
Vein Pattern: Vein pattern identifies an individual by the unique pattern of veins on his palm. Palm vein patterns are unique from one person to the next and, except for the size, they do not change with time. Here the palm is first illuminated by an infra-red light. The veins just beneath the skin of the palm then emit a black reflection, giving a picture of the veins in the palm. Using an algorithm, a pattern is extracted from this picture and checked against patterns already stored in the system. If there is a match, the person's identity is confirmed.
Hand and Finger Geometry: One of the oldest biometric methods after fingerprint recognition, hand geometry has been in use for over three decades. It involves analysing and measuring physical characteristics of the user’s hand and fingers, from a three dimensional perspective. Spatial geometry is examined as the user puts his hand on the sensor’s surface and uses guiding poles between the fingers to properly place the hand and initiate the reading. Finger geometry usually measures two or three fingers.
Hand geometry is essentially based on the fact that virtually every individual's hand is shaped differently and over the course of time the shape of a person's hand does not change significantly. The basic principle of operation behind the use of hand geometry is to measure the physical geometric characteristics of an individual's hand. From these measurements a profile or 'template' is constructed which is then used to compare against subsequent hand readings of the user.
Hand geometry scanning devices are of two types: mechanical and image-edge detection. Both methods are used to measure specific characteristics of a person's hand such as length of fingers and thumb, width, and depth. Hand geometry devices employed today take over 90 measurements of the length, width, thickness, and surface area of a person’s hand and fingers. This entire process takes only one second. To capture the measurements of a person’s hand, a charge-coupled device (CCD) digital camera is used to record the hand's three-dimensional shape. Unlike fingerprint imaging systems, hand geometry readers do not take into account natural and environmental surface details, such as lines, scars, dirt, and fingernails.
Hand geometry technology posses one of the smallest reference templates in the biometric field, which is generally under ten bytes long. There are other advantages of using hand geometry as a solution to general security issues such as:
- Speed of operation,
- Reliability and accuracy,
- User - friendliness, and
- Ease of integration into an existing system.
Hand geometry is a well-developed technology that has been thoroughly field-tested and is easily accepted by users. It offers a good balance between performance-characteristics and ease of use. The system is employed where robustness and low-cost are of primary concern. Accuracy can be high if desired, whilst flexible performance tuning and configuration can accommodate a wide range of applications. Hand geometry applications are finding their way into mainstream industries including child day care centres, health clubs, and universities.
Applications of Biometric Devices:
- Leading car manufacturers use fingerprint recognition as a requirement for ignition of the engine.
- Disney World uses a fingerprint scanner to verify season-pass holders entering the theme park.
- GM and Hertz uses voice identification technology to protect their computer facilities.
- NetNanny has developed a user identification system using keystroke dynamics.
- British Employment Services use signature verification to verify an individual who is claiming benefits.
- Pentonville Prison in England employs signature identification to prevent prisoner's signing off food against other prisoner's accounts.
- Heathrow airport uses iris recognition to check the identity of travellers entering UK.
- Iris scanners were used in the Winter Olympics in Nagano to identify biathlon participants before they were granted access to their rifles.
- The US Immigration and Naturalization Service currently allows international travellers to bypass long lines at busy airports by using an automated kiosk with a hand geometry recognition system.
- Child day care centres use hand geometry systems to verify the identity of parents.
- Hospitals use hand geometry systems to monitor payroll accuracy and access control.
- Fujitsu has developed a mouse that can verify a person's identity by recognizing his pattern of blood veins.
Standards: The biometrics industry includes more than 150 separate hardware and software vendors, each with their own proprietary interfaces, algorithms, and data structures. Standards are emerging to provide a common software interface to allow sharing of biometric templates and permit effective comparison and evaluation of different biometric technologies. The BioAPI standard defines a common method for interfacing with a given biometric application. It is an open-systems standard written in C, consisting of a set of function calls to perform basic actions common to all biometric technologies, such as enrol user, verify asserted identity (authentication), and discover identity.
In order to promotes interoperability of biometric-based application programs and systems developed by different vendors the National Institute of Standards and Technology (NIST) has developed The Common Biometric Exchange File Format (CBEFF) by allowing biometric data interchange between different system components and systems. It promotes interoperability of biometric-based application programs and systems, provides forward compatibility for technology improvements and simplifies the software and hardware integration process. These data can be placed in a single file used to exchange biometric information between different system components or between systems.
The Right Choice: In order to decide on which biometric system to implement for a particular purpose, a lot many things need to be taken into account. It is desired that these systems have the following properties:
- Automatically measurable,
- Robust, and
- Distinct.
The characteristic or trait should be easily presentable to the sensor for conversion to a quantifiable, digital format. Robustness refers to the extent to which the characteristic or trait may be subject to significant changes over time. These changes may occur as a result of age, injury, illness, occupational use, or chemical exposure. A highly robust biometric does not change significantly over time. For example, the iris, which changes very little over one’s lifetime, is more robust than one’s voice. Distinctiveness is a measure of the variations or differences in the biometric pattern among the general population. The higher the degree of distinctiveness, the more individual is the identifier. A low degree of distinctiveness indicates a biometric pattern found frequently in the general population. The iris and the retina have a higher degree of distinctiveness than hand or finger geometry.
It must also be noted that there are many limitations to using a single biometric such as noise in sensed data, lack of permanence, easier to cheat, etc. This is where multimodal biometrics comes into play. It is the combination of multiple sensors, matchers or characteristics for enhanced verification and identification. It is more difficult to cheat as compared to a single biometric and has an improved challenge response authentication.
Different technologies may suggest themselves for different applications depending upon perceived user profile, the need to interface with other systems or databases, environmental conditions and a host of other parameters specific to the application at hand. Whilst there are some obvious areas of application for certain technologies, it is probably wise to seek specialist advice in this context. Biometric parameters such as hand geometry, fingerprint scanning, iris scanning, voice verification, retinal scanning, signature verification and others are all well established with their own particular characteristics, which will suit different circumstances and applications accordingly. Their practical performance might in many instances be better than one supposes, with the cost of implementation realistic in comparison with conventional methods such as token technology.
Future Prospects: Biometry is one of the most promising and life-altering technologies in existence today. It is all set to change the way we live in the future. Some of the emerging biometrics technologies in the near future are:
- Ear shape identification - measures the shape and geometry of the ear. Ears have more identification richness than any other part of the human body except fingerprints. They do not change significantly from the time the subject reaches adult age.
- Body odour identification - body odour can be digitally recorded for identification.
- Body salinity identification – exploits the natural level of salinity in the human body. An electric field is passed through the body on which data can be carried.
- EEG Fingerprint - the baseline brainwave pattern of every individual is unique and thus could feasibly meet the qualifications of a biometric.
- DNA matching - the "Ultimate" biometric technology that would produce proof-positive identification of an individual. This technology is still not considered a "biometric" technology and is years away from any kind of implementation.
Conclusion: Even though the accuracy of the biometric techniques is not perfect yet, there are many mature biometric systems available in the market today. Many successful applications of biometric technology currently exist. This technology has proven capable of decreasing costs and increasing convenience for both users and system administrators. Further, these systems are capable of increasing both privacy and identity security. There is no reason why these devices could not currently be used within the financial services community for internal applications and infrastructure protection, such as for access control to sensitive areas and computers. The major impediment to universal implementation at the consumer level is the wide variety of competing, vendor-proprietary devices, all without general standardization. This cacophony of devices, however, further serves as a protection to privacy, preventing any one measurement to be used to access non-communicating systems.
Utilized alone or integrated with other technologies such as smart cards, encryption keys and digital signatures, biometrics is set to pervade all aspects of the economy and our daily lives. Utilizing biometrics for personal authentication is convenient and more secure than current keys, passwords and PINs. With rapid progress in electronic and Internet commerce, there is a growing need to authenticate the identity of a person for secure transaction processing. These technologies are the foundation of an extensive array of highly secure, fast, accurate and user-friendly identification and personal verification solutions.
A complete systems approach that addresses a variety of security, functional, operational and cost considerations is necessary for the overall growth of this sector. It is an exciting technology that can be used in robust ways by both the public and private sectors. It may also be used to authenticate animals. A biometric system for authentication of race-horses is used in Japan and a South African company that imports pedigree dogs uses a biometric technique to verify the dogs being imported.
Privacy concerns are foremost in everyone's minds. Its data are separate and distinct from personal information and thus the templates cannot be reverse-engineered to recreate this information. Precisely because of these inherent attributes, biometrics is an effective means to secure privacy and deter identity theft. This technology can be used for access control purposes, thereby restricting unauthorized personnel from gaining access to sensitive personal information. They are used in a variety of industries and circumstances. With the improvement in technology and fall in prices the field of biometrics is evolving. As a result, funding is becoming more widespread and the development of biometrics is on the rise. Each of us individually and all of us collectively will benefit from this technology in more ways than one.
References:
- The Biometric Consortium - www.biometrics.org
- National Institute of Standards and Technology - www.nist.gov
- The Biometric Catalog - www.biometricscatalog.org
- ITPro - www.computer.org
- Western Carolina University - www.wcu.edu
- Georgia Institute of Technology - www.gatech.edu
- New York University - www.nyu.edu
- Faculty of Informatics, Masaryk University.
- Biometric Systems Lab - University of Bologna.
- National Biometric Test Centre - San Jose State University.
- Atos Origin.
- U.S. Subcommittee on Domestic and International Monetary Policy.
- Biometric Access & Neural Control - ICDRI, AT&T Global Network Services.
- RAND Public Safety and Justice.
Homework Help: Science: Biology
For Further Reading
|
