BEEE Ist Biometric Technology Abstract: This paper discusses several Biometric scan technologies: finger- scan, facial scan and retinal-scan. We discuss the recent history of Biometrics and how it has been influenced by such pseudo-sciences as Phrenology, the study of human skull characteristics and Anthropometry, the study of human body measurement. We discuss how finger-scan technology was influenced by French and British police advancements in the nineteenth century and still remain the most widely used Biometric technology today. Facial-scan technology is beset with privacy concerns especially when this technology is applied to unsuspecting crowds. Retinal-scan technology, is a relatively new entrant to the biometric field and offers significant promise. One of the continuing challenges for the biometric industry is to define the environment in which the technology provides the strongest benefit to individuals and institutions. For the security officer, the challenge will be to demonstrate to upper management that the costs associated with deployment outweigh the risks and costs. 1
Transcript
BEEE Ist Biometric Technology
Abstract:
This paper discusses several Biometric scan technologies: finger-scan, facial scan and retinal-scan. We discuss the recent history of Biometrics and how it has been influenced by such pseudo-sciences as Phrenology, the study of human skull characteristics and Anthropometry, the study of human body measurement. We discuss how finger-scan technology was influenced by French and British police advancements in the nineteenth century and still remain the most widely used Biometric technology today. Facial-scan technology is beset with privacy concerns especially when this technology is applied to unsuspecting crowds. Retinal-scan technology, is a relatively new entrant to the biometric field and offers significant promise. One of the continuing challenges for the biometric industry is to define the environment in which the technology provides the strongest benefit to individuals and institutions. For the security officer, the challenge will be to demonstrate to upper management that the costs associated with deployment outweigh the risks and costs.
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Biometric Technology
What is Biometric Technology?
Biometrics (or biometric authentication) consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioural traits. In computer science, in particular, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance.
Biometrics literally means life measurement and is associated with utilization of distinctive physiological characteristics for identifying individuals. Though most important application related with biometrics is that of security, it is used as the computer interface too. A range of Biometric applications are being used for authenticating person’s identity. With the use of various features including fingerprints, face, signature, and iris, a person is identified.
Classes of Biometric Technology:
There are two types of classes of Biometric Technology:
Physiological
Behavioral
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Physiological
Related to the shape of the body. Examples include, but are not limited to fingerprint, face recognition, DNA, Palm print, hand geometry, iris recognition, which has largely replaced retina, and odour/scent.
Behavioral
Related to the behaviour of a person. Examples include, but are not limited to typing rhythm, gait, and voice. Some researchers have coined the term behaviometrics for this class of biometrics.
Over view of further classification of Biometric Technology:
(Fig no: 1 Classification of Biometric Technology)
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History (Origins of Biometric Technology):
BC 200s - China
Chinese records from the 221-206 BC Qin Dynasty include details about using handprints as evidence during burglary investigations.
AD 1400s - Persia
The 14th century Persian book "Jaamehol-Tawarikh" (Universal History), attributed to Khajeh Rashiduddin Fazlollah Hamadani (1247-1318), includes comments about the practice of identifying persons from their fingerprints.
1600s - Europe
In a "Philosophical Transactions of the Royal Society of London" paper in 1684, Dr. Nehemiah Grew was the first European to publish friction ridge skin observations.
Dutch anatomist Govard Bidloo's 1685 book, "Anatomy of the Human Body" also described friction ridge skin (papillary ridge) details.
1858 - Herschel
The English first began using fingerprints in July of 1858, when Sir William James Herschel, Chief Magistrate of the Hooghly district in Jungipoor, India, first used fingerprints on native contracts. On a whim, and without thought toward ersonal identification, Herschel had Rajyadhar Konai, a local businessman, impress his hand print on a contract.
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1882 - Thompson
In 1882, Gilbert Thompson of the U.S. Geological Survey in New Mexico, used his own thumb print on a document to help prevent forgery. This is the first known use of fingerprints in the United States. Click the image below to see a larger image of an 1882 receipt issued by Gilbert Thompson to "Lying Bob" in the amount of 75 dollars.
(Fig no: 2 Letter with thumb verification by Thompson)
1882 - Bertillon
Alphonse Bertillon, a Clerk in the Prefecture of Police of at Paris, France, devised a system of classification, known as anthropometry or the Bertillon System, using measurements of parts of the body. Bertillon's system included measurements such as head length, head width, length of the middle finger, length of the left foot; and length of the forearm from the elbow to the tip of the middle finger.
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(Fig no 3,4&5 showing how to take biometric in old times?)
1901 - Henry
The Fingerprint Branch at New Scotland Yard (Metropolitan Police) was created in July 1901 using the Henry System of Fingerprint Classification.
1905 U.S. Army begins using fingerprints. U.S. Department of Justice forms the Bureau of Criminal Identification in Washington, DC to provide a centralized reference collection of fingerprint cards.
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1907 U.S. Navy begins using fingerprints. U.S. Department of Justice's Bureau of Criminal Identification moves to Leavenworth Federal Penitentiary where it is staffed at least partially by inmates.
1908 U.S. Marine Corps begins using fingerprints.
2014 - World's Largest Database
As of March 2014, the Unique Identification Authority of India operates the world's largest fingerprint (multi-modal biometric) system, with over 560 million fingerprint, face and iris biometric records. UIAI plans to collect as many as 600 million multi-modal record by the end of 2014. India's Unique Identification project is also known as Aadhaar, a word meaning "the foundation" in several Indian languages. Aadhaar is a voluntary program, with the ambitious goal of eventually providing reliable national ID documents for most of India's 1.2 billion.
(Reference http://onin.com/fp/fphistory.html)
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Biometric System Component:
Sensor
Collects data and converts the information to a digital format.
Signal processing algorithms
Perform quality control activities and develop the biometric template.
Data storage
Keeps information that new biometric templates will be compared to.
Matching algorithm
Compares the new biometric template to one or more templates in data storage.
Decision process
Uses the results from the matching component to make a system-level decision (either automated or human-assisted).
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Working of Biometrics Technology:
All biometric systems works in a four-stage process that consists of the following steps:
Capture A biometric system collects the sample of biometric features like fingerprint,
voice etc of the person who wants to login to the system.
Extraction
The data extraction is done uniquely from the sample and a template is created. Unique features are then extracted by the system and converted into a digital biometric code. This sample is then stored as the biometric template for that individual.
Comparison
The template is then compared with a new sample. The biometric data are then stored as the biometric template or template or reference template for that person.
Match/Non-Match
The system then decides whether the features extracted from the new sample are a match or a non-match with the template. When identity needs checking, the person interacts with the biometric system, a new biometric sample is taken and compared with the template. If the template and the new sample match, the person's identity is confirmed else a non-match is confirmed.
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Biometric Authentication System and its functional components:
The Biometric authentication system includes three layered architecture:
Enroll A sample is captured from a device, processed into a usable form from
which a template is constructed, and returned to the application.
Verify
One or more samples are captured, processed into a usable form, and then matched against an input template. The results of the comparison are returned.
IdentifyOne or more samples are captured, processed into a usable form, and
matched against a set of templates. A list is generated to show how close the samples compare against the top candidates in the set.
(Fig no: 6 Example of Biometric Authentification System)
Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints. Fingerprints are one of many forms of biometrics used to identify individuals and verify their identity.
Background
The analysis of fingerprints for matching purposes generally requires the comparison of several features of the print pattern. These include patterns, which are aggregate characteristics of ridges, and minutia points, which are unique features found within the patterns. It is also necessary to know the structure and properties of human skin in order to successfully employ some of the imaging technologies.
Patterns
The three basic patterns of fingerprint ridges are the arch, loop, and whorl.
An arch is a pattern where the ridges enter from one side of the finger, rise in the center forming an
arc, and then exit the other side of the finger. The loop is a pattern where the ridges enter from one
side of a finger, form a curve, and tend to exit from the same side they enter. In the whorl pattern,
ridges form circularly around a central point on the finger. Scientists have found that family
members often share the same general fingerprint patterns, leading to the belief that these patterns
The major Minutia features of fingerprint ridges are: ridge ending,
bifurcation, and short ridge (or dot). The ridge ending is the point at which a ridge terminates.
Bifurcations are points at which a single ridge splits into two ridges. Short ridges (or dots) are ridges
which are significantly shorter than the average ridge length on the fingerprint. Minutiae and
patterns are very important in the analysis of fingerprints since no two fingers have been shown to
be identical.
Ridge ending. Bifurcation. Short Ridge (Dot).
(Fig no:7 Shows fingerprint scanning)
Fingerprint Sensor
A fingerprint sensor is an electronic device used to capture a digital image of the fingerprint pattern. The captured image is called a live scan. This live scan is digitally processed to create a biometric template (a collection of extracted features) which is stored and used for matching. This is an overview of some of the more commonly used fingerprint sensor technologies.
Optical
Optical fingerprint imaging involves capturing a digital image of the print using visible light. This type of sensor is, in essence, a specialized digital camera. The top layer of the sensor, where the finger is placed, is known as the touch surface. Beneath this layer is a light-emitting phosphor layer which illuminates the surface of the finger. The light reflected from the finger passes through the phosphor layer to an array of solid state pixels (a charge-coupled device)
which captures a visual image of the fingerprint. A scratched or dirty touch surface can cause a bad image of the fingerprint. A disadvantage of this type of sensor is the fact that the imaging capabilities are affected by the quality of skin on the finger. For instance, a dirty or marked finger is difficult to image properly. Also, it is possible for an individual to erode the outer layer of skin on the fingertips to the point where the fingerprint is no longer visible. It can also be easily fooled by an image of a fingerprint if not coupled with a "live finger" detector. However, unlike capacitive sensors, this sensor technology is not susceptible to electrostatic discharge damage.
Ultrasonic
Ultrasonic sensors make use of the principles of medical ultrasonography in order to create visual images of the fingerprint. Unlike optical imaging, ultrasonic sensors use very high frequency sound waves to penetrate the epidermal layer of skin. The sound waves are generated using piezoelectric transducers and reflected energy is also measured using piezoelectric materials. Since the dermal skin layer exhibits the same characteristic pattern of the fingerprint, the reflected wave measurements can be used to form an image of the fingerprint. This eliminates the need for clean, undamaged epidermal skin and a clean sensing surface.
Capacitance
Capacitance sensors utilize the principles associated with capacitance in order to form fingerprint images. In this method of imaging, the sensor array pixels each act as one plate of a parallel-plate capacitor, the dermal layer (which is electrically conductive) acts as the other plate, and the non-conductive epidermal layer acts as a dielectric.
Passive capacitance
A passive capacitance sensor uses the principle outlined above to form an image of the fingerprint patterns on the dermal layer of skin. Each sensor pixel is used to measure the capacitance at that point of the array. The capacitance varies between the ridges and valleys of the fingerprint due to the fact that the volume between the dermal layer and sensing element in valleys contains an air gap. The dielectric constant of the epidermis and the area of the sensing element are known values. The measured capacitance values are then used to distinguish between fingerprint ridges and valleys.
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Algorithms
Matching algorithms are used to compare previously stored templates of fingerprints against candidate fingerprints for authentication purposes. In order to do this either the original image must be directly compared with the candidate image or certain features must be compared.
Pattern-based (or image-based) algorithms
Pattern based algorithms compare the basic fingerprint patterns (arch, whorl, and loop) between a previously stored template and a candidate fingerprint. This requires that the images be aligned in the same orientation. To do this, the algorithm finds a central point in the fingerprint image and centers on that. In a pattern-based algorithm, the template contains the type, size, and orientation of patterns within the aligned fingerprint image. The candidate fingerprint image is graphically compared with the template to determine the degree to which they match.
Face recognition technology is the least intrusive and fastest biometric technology. It works with the most obvious individual identifier – the human face.
Instead of requiring people to place their hand on a reader (a process not acceptable in some cultures as well as being a source of illness transfer) or precisely position their eye in front of a scanner, face recognition systems unobtrusively take pictures of people's faces as they enter a defined area. There is no intrusion or delay, and in most cases the subjects are entirely unaware of the process. They do not feel "under surveillance" or that their privacy has been invaded.
Technology
Facial recognition analyzes the characteristics of a person's face images input through a digital video camera. It measures the overall facial structure, including distances between eyes, nose, mouth, and jaw edges. These measurements are retained in a database and used as a comparison when a user stands before the camera. This biometric has been widely, and perhaps wildly, touted as a fantastic system for recognizing potential threats (whether terrorist, scam artist, or known criminal) but so far has not seen wide acceptance in high-level usage. It is projected that biometric facial recognition technology will soon overtake fingerprint biometrics as the most popular form of user authentication.
Every face has numerous, distinguishable landmarks, the different peaks and valleys that make up facial features. Each human face has approximately 80 nodal points. Some of these measured by the Facial Recognition Technology are:
Distance between the eyes Width of the nose Depth of the eye sockets The shape of the cheekbones The length of the jaw line
These nodal points are measured creating a numerical code, called a face print, representing the face in the database.
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(Fig no:9 shows Face Recognition)
How it Works
The following four-stage process illustrates the way biometric systems operate:
Capture A physical or behavioral sample is captured by the system during
Enrollment.
Extraction Unique data is extracted from the sample and a template is created.
Comparison The template is then compared with a new sample.
Matching The system then decides if the features extracted from the new sample are
matching or not.
When the user faces the camera, standing about two feet from it. The system will locate the user's face and perform matches against the claimed identity or the facial database. It is possible that the user may need to to move and reattempt the verification based on his facial position. The system usually comes to a decision in less than 5 seconds.
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(Fig no:10 shows How image is scan on biometric system)
Use
Currently gaining support as a potential tool for averting terrorist crimes, facial recognition is already in use in many law enforcement areas. Software has also been developed for computer networks and automated bank tellers that use facial recognition for user verification purposes.
Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of the irides of an individual's eyes, whose complex random patterns are unique and can be seen from some distance.
Not to be confused with another, less prevalent, ocular-based technology, retina scanning, iris recognition uses camera technology with subtle infrared illumination to acquire images of the detail-rich, intricate structures of the iris. Digital templates encoded from these patterns by mathematical and statistical algorithms allow unambiguous positive identification of an individual. Databases of enrolled templates are searched by matcher engines at speeds measured in the millions of templates per second per (single-core) CPU, and with infinitesimally small False Match rates.
Many millions of persons in several countries around the world have been enrolled in iris recognition systems, for convenience purposes such as passport-free automated border-crossings, and some national ID systems based on this technology are being deployed. A key advantage of iris recognition, besides its speed of matching and its extreme resistance to False Matches, is the stability of the iris as an internal, protected, yet externally visible organ of the eye.
(Fig no:11 shows Iris Recognition)
Visible Wavelength(VW) vs Near Infrared(NIR) Imaging
The majority of iris recognition cameras use Near Infrared (NIR) imaging by emitting 750nm wavelength low-power light. This is done because dark-brown eyes, possessed by the majority of the human population, reveal rich structure in the NIR but much less in the visible band (400 - 700nm), and also because NIR light is invisible and unintrusive. A further important reason is that by allowing only this selected narrow band of illuminating light back into the camera via its filters, most of the ambient corneal reflections from a bright environment are blocked from contaminating the iris patterns.
The melanin, also known as chromophore, mainly consists of two distinct heterogeneous macromolecules, called eumelanin (brown–black) and pheomelanin (yellow–reddish). NIR imaging is not sensitive to these chromophores, and as a result they do not appear in the captured images. In contrast, visible wavelength (VW) imaging keeps the related chromophore
information and, compared to NIR, provides rich sources of information mainly coded as shape patterns in iris. Hosseini, et al. provide a comparison between these two imaging modalities and fused the results to boost the recognition rate. An alternative feature extraction method to encode VW iris images was also introduced, which is highly robust to reflectivity terms in iris. Such fusion results are seemed to be alternative approach for multi-modal biometric systems which intend to reach high accuracies of recognition in large databanks.
Operating Principal
An iris-recognition algorithm first has to localize the inner and outer boundaries of the iris (pupil and limbus) in an image of an eye. Further subroutines detect and exclude eyelids, eyelashes, and specular reflections that often occlude parts of the iris. The set of pixels containing only the iris, normalized by a rubber-sheet model to compensate for pupil dilation or constriction, is then analyzed to extract a bit pattern encoding the information needed to compare two iris images. In the case of Daugman's algorithms, a Gabor wavelet transform is used. The result is a set of complex numbers that carry local amplitude and phase information about the iris pattern. In Daugman's algorithms, most amplitude information is discarded, and the 2048 bits representing an iris pattern consist of phase information (complex sign bits of the Gabor wavelet projections). Discarding the amplitude information ensures that the template remains largely unaffected by changes in illumination or camera gain (contrast), and contributes to the long-term usability of the biometric template. For identification (one-to-many template matching) or verification (one-to-one template matching), a template created by imaging an iris is compared to stored template(s) in a database. If theHamming distance is below the decision threshold, a positive identification has effectively been made because of the statistical extreme improbability that two different persons could agree by chance ("collide") in so many bits, given the high entropy of iris templates.
(Fig no: 11 shows An IriScan model 2100 iris scanner)
Hand geometry is a biometric that identifies users by the shape of their hands. Hand geometry readers measure a user's hand along many dimensions and compare those measurements to measurements stored in a file.
Viable hand geometry devices have been manufactured since the early 1980s, making hand geometry the first biometric to find widespread computerized use. It remains popular; common applications include access control and time-and-attendance operations.
Since hand geometry is not thought to be as unique as fingerprints, palm veins or irises, fingerprinting, palm veins and iris recognition remain the preferred technology for high-security applications. Hand geometry is very reliable when combined with other forms of identification, such as identification cards or personal identification numbers. In large populations, hand geometry is not suitable for so-called one-to-many applications, in which a user is identified from his biometric without any other identification.
Based on the data used for personal identification, technologies for reading human hand can be
divided in three categories:
Palm technology
Hand vein technology
Hand geometry and hand shape technology
Palm Technology
The first category is considered the classic approach in the hand biometrics. As mentioned earlier, it is part of dactiloscopy, so methods used here are similar to those used for fingerprints. The size, shape and flow of papillae are measured and minutiae are the main features in the identification process. Image preprocessing and normalization in this category gives us binary image containing papillae and their distances. Because of the different lightning when taking an image, the palm can be divided into five areas, although strictly medically speaking, if we consider the muscles, it has only three areas. The areas of the palm are: lower palm, middle palm, upper palm, thenar (thumb part) and hypothenar (little finger part).
Second category uses similar approach for capturing hand image, but instead of using ordinary camera or scanner it rather uses specialized devices containing scanners with infrared light or some other technology that can be used for retrieving image of veins under the human skin. Hand vein biometrics is gaining popularity in the last years and it is likely that this will be one of the main biometric characteristics for the future. Using contactless approach for capturing the structure of human veins gives promising results in this field.
(Fig no:13 shows Vein scanning Technology)
Hand Geometry and Hand Shape Technology
Every human hand is unique. In 2002, thirty global features of hand geometry were defined. These features are very exact, but can be represented as global features of contact-less 2D hand geometry.
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(Fig no:14 shows 2D Hand Geometry)
Features which authors defined in their works and shown in the figure no 14 are following:
Those features became typical features in the systems that use hand geometry in the identification
or authentication process.
Fotak and Karlovčec New Hand Technology
Fotak and Karlovčec presented a different method of feature extraction. They decided to use mathematical graphs on the two-dimensional hand image. Hand image was normalized by using basic morphological operators and edge detection. They created a binary image from the image captured with an ordinary document scanner. On the binary image the pixel values were analyzed to define the location of characteristic points. They extracted 31 points, shown in the fig no 15.
(Fig no:15 shows Hand scanning by plotting Graph)
For the hand placement on y-axis a referential point on the top of the middle finger was used. The location of that point was determined by using the horizontal line y1. Using that line, authors defined 6 points that represents the characteristic points of index, middle and ring finger. Using lines
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y2 and y3 they extracted enough characteristic points for four fingers. Thumb has to be processed in the different manner. To achieve that the right-most point of the thumb had to be identified. Using two vertical lines they found the edges of the thumb. By analyzing points on those lines and their midpoints the top of the thumb could be extracted. Example of the thumb top extracting is shown in the fig no:16
In order to get enough information for their process, each hand had to be scanned four times. For each characteristic point authors constructed the complete graph. The example of characteristic points from four scans and the corresponding complete graph of one point are shown fig no 16 and 17 respectively.
(Fig no: 16 and 17 Characteristic points of the four scanning of the hand, The complete graph of one characteristic point)
The number of edges in the complete graph is well known. In order to construct minimum spanning tree this graph needed to be weighted graph. The weights are distances between two graph vertices that are connected with an edge. Distances were measured using Euclidean distance. In the end, Prim algorithm was used to construct minimum spanning tree of one characteristic point. The same procedure was made for each of 31 points. The example of minimum spanning tree of one characteristic point and all minimum spanning trees.
The verification process is made by comparing every point minimum spanning tree with the location of currently captured corresponding point. The results of the system are very promising for future development, and are FAR = 1.21% and FRR = 7.75%.
Signature verification is the process used to recognize an individual’s hand-written signature.
Types of Signature Verification:
Signature Verification
Dynamic Signature Verification
Dynamic Signature Verification
Verification technology uses the behavioral biometrics of a hand written signature to confirm the identity of a computer user. This is done by analyzing the shape, speed, stroke, pen pressure and timing information during the act of signing. Natural and intuitive, the technology is easy to explain and trust. Dynamic signature verification technology uses the behavioral biometrics of a hand written signature to confirm the identity of a computer user. Unlike the older technologies of passwords and keycards - which are often shared or easily forgotten, lost, and stolen - dynamic signature verification provides a simple and natural method for increased computer security and trusted document authorization.
Signature Verification
Signature verification is natural and intuitive. The technology is easy to explain and trust. The primary advantage that signature verification systems have over other types of biometric technologies is that signatures are already accepted as the common method of identity verification. This history of trust means that people are very willing to accept a signature based verification system.
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(Fig no: 18 shows Signature scanning)
As a replacement for a password or a PIN number, dynamic signature verification is a biometric technology that is used to positively identify a person from their handwritten signature.
There is an important distinction between simple signature comparisons and dynamic signature verification. Both can be computerized, but a simple comparison only takes into account what the signature looks like. Dynamic signature verification takes into account how the signature was made. With dynamic signature verification it is not the shape or look of the signature that is meaningful, it is the changes in speed, pressure and timing that occur during the act of signing. Only the original signer can recreate the changes in timing and X, Y, and Z (pressure).
A pasted bitmap, a copy machine or an expert forger may be able to duplicate what a signature looks like, but it is virtually impossible to duplicate the timing changes in X, Y and Z (pressure). The practiced and natural motion of the original signer would required to repeat the patterns shown.
There will always be slight variations in a person’s handwritten signature, but the consistency created by natural motion and practice over time creates a recognizable pattern that makes the handwritten signature a natural for biometric identification.
Signature-Scan: How It Works
Signature-scan technology utilizes the distinctive aspects of the signature to verify the identity of individuals. The technology examines the behavioral components of the signature, such as stroke order, speed and pressure, as opposed to comparing visual images of signatures. Unlike traditional signature comparison technologies, signature-scan measures the physical activity of signing. While a system may also leverage a comparison of the visual appearance of a signature, or “static signature,” the primary components of signature-scan are behavioral.
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The signature, along with the variables present during the signing process, is transmitted to a local PC for template generation. Verification can take place against a local PC or a central PC, depending on the application. In employee-facing signature-scan applications such as purchase order authentication, local processing may be preferred; there may be just a single PC used for such authorization. For customer-facing applications, such as retail or banking authentication, centralized authentication is likely necessary because the user may sign at one of many locations.
The results of signature-scan comparisons must be tied into existing authentication schemes or used as the basis of new authentication procedures. For example, in a transactional authentication scenario, the “authorize transaction” message might be sent after a signature is acquired by a central PC. When signature-scan is integrated into this process, an additional routine requires that the signature characteristics be successfully matched against those on file in order for the “authorize transaction” message to go forward. In other applications, the results of a signature-scan match may simply be noted and appended to a transaction. For example, in document authentication, an unsuccessful comparison may be flagged for future resolution while not halting a transaction. The simplest example would be a signature used for handheld device login: the successful authentication message merely needs to be integrated into the login module, similarly to a PIN or password.
Signature-Scan: Strengths and Weaknesses
Signature-Scan has several strengths. Because of the large amount of data present in a signature-scan template, as well as the difficulty in mimicking the behavior of signing, signature scan-technology is highly resistant to imposter attempts. As a result of the low False Acceptance Rates (FAR), a measure of the likelihood that a user claiming a false identity will be accepted, deployers can have a high confidence level that successfully matched users are who they claim to be. Signature-scan also benefits from its ability to leverage existing processes and hardware, such as signature capture tablets and systems based on public key infrastructure (PKI), a popular method for data encryption. Since most people are accustomed to providing their signatures during customer interactions, the technology is considered less invasive than some other biometrics.
However, signature-scan has several weaknesses. Signature-scan is designed to verify subjects based on the traits of their unique signature. As a result, individuals who do not sign their names in a consistent manner may have difficulty enrolling and verifying in signature-scan. During enrollment subjects must provide a series of signatures that are similar enough that the system can locate a large percentages of the common characteristics between the enrollment signatures. During verification enough characteristics must remain constant to determine with confidence that the authorized person signed. As a result, individuals with muscular illnesses and people who sometimes sign with only their initials might result in a higher False Rejection Rate (FRR), which measures the likelihood that a system will incorrectly reject an authorized user.
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Typical Signature-Scan Applications
Signature-scan is implemented in situations where signature or written input processes are already in place. These applications include contract execution, formal agreements, acknowledgement of services received, access to controlled documents, etc.
As the act of signing documents becomes more integrated with electronic capture processes - signing on acquisition tablets, using special styluses, etc. - the opportunity for biometric authentication will increase dramatically. As of today, there are few acquisition devices deployed in operational environments capable of capturing biometric data. Note that signature-scan is not the same as signature capture, currently used in various point-of-sale systems.
Signature-Scan Market Size
Though it is one of the least frequently deployed technology in the biometric market today, signature-scan usage will increase, as a complement to static signature capture, through 2005. Though a handful of vendors sell signature-scan, these firms will need to show the success of the technology in more high-profile settings. As applications for contract execution, formal agreements and access to controlled documents are demonstrated, signature-scan revenues are projected to grow from $3.0m in 2000 to $101.1m in 2005. Signature-scan revenues are expected to comprise approximately 5% of the entire biometric market.
Voice recognition is an automated method of using vocal characteristics to identify individuals using a pass-phrase. The technology itself is not well-developed, partly because background noise affects its performance. Additionally, it is unclear whether the technologies actually recognize the voice or just the pronunciation of the pass-phrase (password) used to identify the user. The telecommunications industry and the National Security Agency (NSA) continue to work to improve voice recognition reliability. A telephone or microphone can serve as a sensor, which makes this a relatively cheap and easily deployable technology.
Voice recognition systems operate by digitizing a profile of a person's speech to produce a “voice print,” something which is referred to each time that person tries to gain access to secure data. This biometric system uses technology which reduces each spoken word into smaller segments such as syllables. Within these smaller segments, each part has three or four dominant tones that can be captured in a digital form and plotted on a spectrum. This table of tones then yields the speaker's unique voiceprint. The voiceprint is then stored as a table of numbers, where the presence of each dominant frequency in each segment is expressed as a binary number. Since all table entries are either a 1 or 0, each column can be read bottom to top as a long binary code. When a person speaks his or her secret phrase, the code word or words are extracted and compared to the stored model for that person. When a user attempts to gain access to protected data, their secret phrase is compared to the previously stored voice model and all other voiceprints stored in the database. If the voice matches according to the system’s “error correction” number, then it will be determined whether or not the person is authenticated.
Voice Recognition Algorithm
Voice recognition is able to work because of the algorithm it performs to digitally verify if the voice matches. This can be done in several different ways, but in our case, the first step is when the digital signal is passed through a low-order filter to spectrally flatten the signal and to make it less susceptible to infinite precision effects. The transfer function of this filter is:
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The value for a usually ranges from 0.9 to 1.0. For this particular example, a = 0.9375.
The signal is then put into frames which are 256 samples long. This corresponds to about 23 ms of sound per frame. Each frame is then put through a Hamming window. Windowing is used to minimize the discontinuities at the beginning and end of each frame. The Hamming window has the form:
where N is the number of samples per frame. In this system N = 256.
In the third step, each windowed frame is auto-correlated. This is done to minimize the mean square estimation error done in the LPC step. The equation for autocorrelation is:
where p is the order of the LPC coefficients. p = 13 in this system.
The features for each frame are extracted by linear predictive coding (LPC) which is represented by the following equation:
where Sn is the nth speech sample, and the ai the predictor coefficients, and Sn is the prediction of the nth value of the speech signal.
This is a finite-order all-pole transfer function whose coefficients accurately indicate the instantaneous configuration of the vocal tract.
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Cepstral coefficients are obtained from the LPC coefficients. Cepstral coefficients are used since they are known to be more robust and reliable than LPC coefficients, PARCOR coefficients, or the log area ratio coefficients. Recursion is used on this equation to attain the Cepstral coefficients
Here p = 13, a k are the LPC coefficients, and c(i) are the Cepstral Coefficients.
The Cepstral coefficients are then passed through a parameter-weighting step to minimize their sensitivities. Low-order Cepstral coefficients are sensitive to the overall spectral slope and high-order Cepstral coefficients are sensitive to noise and other forms of noise-like variability. The weighted Cepstral coefficients are in the form:
where w m is given by the following equation:
Again p = 13 for our system.
The result is an i x j matrix, where i is the order of the LPC and j is the number of frames. Now that the important characteristics are extracted, the results can be used by VQ and DTW to compare the speaker and word respectively. Thus if VQ and DTW match, then the system should accept the voice as the authenticated user.
A keystroke biometric system for long-text input was developed and evaluated foridentification and authentication applications. The system consists of a Java applet to collectraw keystroke data over the Internet, a feature extractor, and pattern classifiers to makeidentification or authentication decisions.
How Keystroke Software works?
Keystroke dynamics measures the series of key down and key up event timings while the user types a string. For example, if a user’s password is ‘password’ then key down and key up events are captured for each character.
These raw measurements can be recorded from almost any keyboard and can be recorded to determine Dwell time (the time between key down and key up) and Flight time (the time from “key down” to the next “key down” to the time between one “key up” and the next “key up”) as represented in the figure below.
(Fig no:19 Shows Dwell and Flight Time)
Once the keystroke timing data is captured, the recorded keystroke timing data is then processed through a unique neural algorithm, which determines a primary pattern for future comparison. As with any biometric technology applied to an authentication function, the technology is used for two major functions: enroll and verify user credentials.
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Enrolment: Building A Biometric Template
A template that represents the unique biometric signature of the user is derived once nine multiple valid patterns are acquired and processed. For example, the figure below represents the template for a user typing the character string ‘biopassword.’
BioPassword can accept a minimum of 8 characters (however, a minimum of 12 is recommended) and can accept from 1-to-6 input fields. Thus, a biometric template could be generated from a single email address, phrase or a combination of userID and password.
Authentication: Validating Biometric Timings
As part of the authentication process, the user types an authentication attempt and this sample is compared against the biometric template created during the enrolment process. Based on keystroke timings (and their fit to the stored template) a ‘biometric score’ is returned as the result of the comparison process. The score may then be used for making monitoring and/or access control decisions.
(Fig no 20 shows unauthorized and authorized users)
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Advantages of Biometrics Technology:
Increase security - Provide a convenient and low-cost additional tier of security.Reduce fraud by employing hard-to-forge technologies and materials. For e.g.Minimise the opportunity for ID fraud, buddy punching.
Eliminate problems caused by lost IDs or forgotten passwords by using physiological attributes. For e.g. Prevent unauthorized use of lost, stolen or "borrowed" ID cards.
Reduce password administration costs.
Replace hard-to-remember passwords which may be shared or observed.
Integrate a wide range of biometric solutions and technologies, customer applications and databases into a robust and scalable control solution for facility and network access
Make it possible, automatically, to know WHO did WHAT, WHERE and WHEN! Offer significant cost savings or increasing ROI in areas such as Loss Prevention or Time & Attendance.Unequivocally link an individual to a transaction or event.
(Fig no:21 shows Advantages of Biometric Technology)
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Disadvantages of a Biometric System:
The finger print of those people working in Chemical industries are often affected. Therefore these companies should not use the finger print mode of authentication.
It is found that with age, the voice of a person differs. Also when the person has flu or throat infection the voice changes or if there are too much noise in the environment this method may not authenticate correctly. Therefore this method of verification is not workable all the time
For people affected with diabetes, the eyes get affected resulting in differences.
Biometrics is an expensive security solution.
(Fig no: 21 shows Disadvantages of Biometric Technology)
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Conclusion:
There are several types of biometrics, and each has its advantages and drawbacks. Depending on what level of security and what do you want to provide, you have to make the good choice. Biometrics implies that you have to face some ethics and law considerations. But if you can go through this problem, it can provide you a very good, secure and easy way of authenticate people. We think that with the improvement of the actual techniques, it will become one of the standards in the authentication methods in a close future. Nevertheless, without a control by some laws, it would be a mess, because commercial company could use biometric to target people (as it is done actually on Internet), and even sell information to other companies.
Biometrics is the tomorrow authentication’s method, but a lot of work has to be done on both technical and ethical sides. There’s no doubt about it: biometrics has come a long way from the first experimental devices to recent commercial systems featuring a reasonably balanced combination of matching performance and ease of use. However, there is still much to be done: customers are scared off by high failure-to-enrol and false non match rates as well as incompatibilities. Furthermore, system security as a whole needs more care to be taken of.
Introduction to Computer Security (Matt Bishop) Network Security- Private Communication in a public world (Charlie Kaufman, Radia Perlman, Mike Spenicer)
