Fingerprint

Fingerprint recognition is the oldest and most widely deployed biometric technology, relying on ridge patterns and minutiae points to identify and verify individuals across law enforcement, access control, and consumer applications.

Among all the biometric techniques, fingerprint-based identification is the oldest method, and it has been successfully used in numerous applications. Everyone is known to have unique, immutable fingerprints. A fingerprint is made up of a series of ridges and furrows on the surface of the finger. The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows, as well as the minutiae points. Minutiae points are local ridge characteristics that occur at either a ridge bifurcation or a ridge ending.

Fingerprint scanning is the acquisition and recognition of a person's fingerprint characteristics for identification purposes. This allows the recognition of a person through quantifiable physiological characteristics that verify the identity of an individual. There are basically two different types of finger-scanning technology that make this possible. One is an optical method, which starts with a visual image of a finger. The other uses a semiconductor-generated electric field to image a finger. There are a range of ways to identify fingerprints. They include traditional police methods of matching minutiae, straight pattern matching, moiré fringe patterns, and ultrasonics.

There is a greater variety of fingerprint devices available than for any other biometric. Fingerprint recognition is the front-runner for mass-market biometric ID systems. Fingerprint scanning has a high accuracy rate when users are sufficiently trained. Fingerprint authentication is a good choice for in-house systems, where enough training can be provided to users and where the device is operated in a controlled environment. The small size of the fingerprint scanner, its ease of integration — it can be easily adapted to keyboards — and, most significantly, its relatively low cost make it an affordable, simple choice for workplace access security.

Plans to integrate fingerprint scanning technology into laptops using biometric technology include a single chip using more than 16,000 location elements to map a fingerprint from the living cells that lie below the top layers of dead skin. Therefore, the reading is still detectable if the finger has calluses, or is damaged, worn, soiled, moist, dry, or otherwise hard to read — a common obstacle. This subsurface capability eliminates most acquisition or detection failures.

With any security system, users will wonder whether a fingerprint recognition system can be beaten. In most cases, false negatives (a failure to recognize a legitimate user) are more likely than false positives. Overcoming a fingerprint system by presenting it with a "false or fake" fingerprint is likely to be a difficult endeavor. However, such scenarios are attempted, and the sensors on the market use a variety of means to counter them. For instance, someone may attempt to use latent print residue on the sensor just after a legitimate user accesses the system. At the other end of the scale, there is the gruesome possibility of presenting a finger to the system that is no longer connected to its owner. Therefore, sensors attempt to determine whether a finger is live and not made of latex (or worse). Detectors for temperature, blood-oxygen level, pulse, blood flow, humidity, or skin conductivity may be integrated.

Unfortunately, no technology is perfect — false positives and spoiled readings do occur from time to time. But for those craving to break free from the albatross that the password has become, both as a security and a time-management issue, fingerprint scanners are worth looking into. It is estimated that 40 percent of helpdesk calls are password related. Whether incorporated into a keyboard or mouse, or used as a standalone device, scanners are more affordable than ever, allow encryption of files keyed to a fingerprint, and can, perhaps most importantly, help minimize stress over a stolen laptop.

Fingerprint matching techniques can be placed into two categories: minutiae-based and correlation-based. Minutiae-based techniques first find minutiae points and then map their relative placement on the finger. However, there are some difficulties with using this approach. It is difficult to extract the minutiae points accurately when the fingerprint is of low quality. This method also does not take into account the global pattern of ridges and furrows. The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach. However, it has some of its own shortcomings. Correlation-based techniques require the precise location of a registration point and are affected by image translation and rotation.

Fingerprint matching based on minutiae has problems in matching differently sized (unregistered) minutiae patterns. Local ridge structures cannot be completely characterized by minutiae. Researchers have tried an alternate representation of fingerprints that captures more local information and yields a fixed-length code for the fingerprint. Matching then hopefully becomes a relatively simple task of calculating the Euclidean distance between the two codes.

Ongoing work aims to develop algorithms that are more robust to noise in fingerprint images and deliver increased accuracy in real time. A commercial fingerprint-based authentication system requires a very low False Reject Rate (FRR) for a given False Accept Rate (FAR). This is very difficult to achieve with any single technique. Methods to pool evidence from various matching techniques are being investigated to increase the overall accuracy of the system. In a real application, the sensor, the acquisition system, and the variation in system performance over time are all very critical. Field testing on a limited number of users is also used to evaluate system performance over a period of time.

Large volumes of fingerprints are collected and stored every day in a wide range of applications, including forensics, access control, and driver's license registration. Automatic recognition of people based on fingerprints requires that the input fingerprint be matched against a large number of fingerprints in a database (the FBI database contains approximately 70 million fingerprints!). To reduce search time and computational complexity, it is desirable to classify these fingerprints accurately and consistently, so that the input fingerprint only needs to be matched against a subset of the fingerprints in the database.

Fingerprint classification is a technique for assigning a fingerprint to one of several pre-specified types already established in the literature, providing an indexing mechanism. Fingerprint classification can be viewed as a coarse-level matching of fingerprints. An input fingerprint is first matched at a coarse level to one of the pre-specified types, and then, at a finer level, it is compared against the subset of the database containing that type of fingerprint only.

Researchers have developed an algorithm to classify fingerprints into five classes: whorl, right loop, left loop, arch, and tented arch. The algorithm separates the number of ridges present in four directions (0 degrees, 45 degrees, 90 degrees, and 135 degrees) by filtering the central part of a fingerprint with a bank of Gabor filters. This information is quantized to generate a FingerCode, which is used for classification. The classification is based on a two-stage classifier, which uses a K-nearest-neighbor classifier in the first stage and a set of neural networks in the second stage.

The classifier has been tested on 4,000 images in the NIST-4 database. For the five-class problem, a classification accuracy of 90% is achieved. For the four-class problem (arch and tented arch combined into one class), a classification accuracy of 94.8% is achieved. By incorporating a reject option, classification accuracy can be increased to 96% for the five-class classification and to 97.8% for the four-class classification, when 30.8% of images are rejected.

A critical step in automatic fingerprint matching is to automatically and reliably extract minutiae from the input fingerprint images. However, the performance of a minutiae extraction algorithm relies heavily on the quality of the input fingerprint images. To ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of the fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module.

A fast fingerprint enhancement algorithm has been developed, which can adaptively improve the clarity of ridge and furrow structures in input fingerprint images, based on the estimated local ridge orientation and frequency. The performance of this image enhancement algorithm has been evaluated using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.

It has been more than 15 years since the introduction of commercial fingerprint authentication systems. Yet they are only now gaining broad acceptance. This should not be surprising. Many technologies required several years before the right combination of factors allowed them to become ubiquitous. Looking back at laptop computers, cell phones, fax machines, pagers, laser printers, and countless other everyday devices, one will realize most had long gestation periods. Biometrics is now at the acceptance crossroads. What will propel it into common usage?

Convenience first — there is the reason end users should use fingerprint authentication in the IT world (i.e., security), and there is the reason they WILL use it (convenience). The simple fact is that passwords don't work very well. They are "no cost" to establish but very expensive to maintain. Just ask the help desk manager at a major corporation. More than 50 percent of all help desk calls are related to passwords that are lost, forgotten, or otherwise unusable. Count all the passwords you use every day, often having to change them once a month. Password administration is a nightmare for IT managers and users alike. Fingerprint authentication eliminates the problem — and the headaches.

Other authentication mechanisms, such as tokens and smart cards, require you to carry something. This is better than a password but easier to lose. Think about losing your credit card or driver's license. Losing your corporate network access card could be a lot worse. Information is valuable and harder to track than money.

Fingerprints can also act as a simple, trusted, and convenient user interface for a well-thought-out security architecture. The two components need each other to provide truly effective security. A user authenticated via fingerprint can take advantage of a solid security system with minimal training.

Simple Truths — Users don't trust what they don't understand. Most IT security concepts are incomprehensible to the average user. Explaining public and private keys, key recovery systems, and digital certificates is beyond the skills of even experienced IT professionals. Most users have no concept of encryption algorithms and their implementations, nor do they want to understand them. Users want simple, trusted security.

Simple, as in placing a finger down. It does not take a security professional to realize that 10 passwords on sticky notes attached to a monitor represent poor security. Most security breaches involve doing the obvious and are often carried out by insiders.

Trusted, as in having stood the test of time. Fingerprints have been used for identification for over 100 years. They are the standard without question. In addition to signatures, fingerprints are the only other form of identification with legal standing. A key issue of trust is privacy. The best way to maintain privacy is to store a template of unique fingerprint characteristics instead of the entire print. This is sufficient for one-to-one or one-to-many matching and eliminates the need for a database of searchable fingerprints.

Emerging Standards — IT professionals insist on standards, multiple sources of supply, and endorsement by industry leaders. This is beginning to happen, but the idea that a small biometrics company could set an industry standard is not realistic. Yet many have tried.

No CIO or IT manager would bet their job or company on a proprietary solution from a small biometrics company. These decision-makers want choice and standards that provide multiple sources of supply and fair competition among vendors. The one exception to this rule is when there has been a major catastrophe, such as a significant loss of money. However, it is tough to build a sustainable business chasing disasters.

Standards need to be set by IT industry leaders, such as Intel, Microsoft, Phoenix Technologies, and the top computer companies. In recent years, many of these large organizations have banded together to begin the process of standardization. This is the first sign of an industry maturing.

Cost — Just as in the early days of desktop computers, when a system cost more than $10,000 and only a few people had systems, now that systems cost less than $1,000, everybody has one. This same "order of magnitude" cost breakthrough has recently occurred with fingerprint technology. What cost $1,000 two years ago is now available for less than $100. Cost alone is not the whole answer, but it is a necessary component of broad market acceptance for this technology.

Complete Solutions — Many companies talk about "complete solutions," but what does this mean? It does not mean a custom, proprietary combination of fingerprint sensor, matching software, and application software — point products and closed solutions are not acceptable. It does mean an open architecture, where the sensor, matching algorithm, and applications are interchangeable and leverageable. Veridicom's OpenTouch architecture embraces this principle and lets the user choose.

Measurable Usefulness — Being able to accurately gauge the usefulness of a fingerprint authentication solution is very important. This technology saves money on password administration, user uptime, and user support. More importantly, fingerprint authentication allows more to be done with a computer. Now, remote secure network access is possible. Electronic commerce makes sense when authentication is trusted. It is a fact that 75 percent of internet users are uncomfortable transmitting their credit card information over the public network. Imagine if this were never an issue. Fingerprint authentication is an enabling technology for trusted e-commerce.

All the market signs point toward acceptance of fingerprint authentication as a simple, trusted, convenient method of personal authentication. Industry leaders are validating the technology through standards initiatives. Cost and performance breakthroughs have transformed fingerprint biometrics from an interesting technology into an easy-to-implement authentication solution. Industry trends such as electronic commerce and remote computing intensify the need for better authentication. Most importantly, users understand and accept the concept. Passwords and tokens are universally disliked. It's hard to get much simpler than a fingerprint.

The aura of criminality that accompanies the term "fingerprint" has not significantly impeded the acceptance of fingerprint technology, because the two authentication methods are very different. Fingerprinting, as the name suggests, is the acquisition and storage of the image of a fingerprint. Fingerprinting was, for decades, the common ink-and-roll procedure used when booking suspects or conducting criminal investigations. More advanced optical or non-contact fingerprinting systems (known as live-scan), which normally use prints from several fingers, are currently the standard for forensic use. They require 250kb per finger for a high-quality image. Fingerprint technology also acquires the fingerprint but does not store the full image. It stores particular data about the fingerprint in a much smaller template, requiring 250-1000 bytes. After the data is extracted, the fingerprint itself is not stored. Significantly, the full fingerprint cannot be reconstructed from the fingerprint template.

Fingerprinting is used in forensic applications for large-scale, one-to-many searches across databases of up to millions of fingerprints. These searches can be completed within a few hours, a tribute to the computational power of AFIS. AFIS (Automated Fingerprint Identification Systems) — commonly referred to as "AFIS systems" (a redundancy) — is a term applied to large-scale, one-to-many searches. Although fingerprint technology can be used in AFIS on 100,000-person databases, it is much more frequently used for one-to-one verification within 1-3 seconds.

Many people think of forensic fingerprinting as an ink-and-paper process. While this may still be done in some locations, most jurisdictions use optical scanners known as livescan systems. There are some fundamental differences between these forensic fingerprinting systems (used in AFIS systems) and the biometric fingerprint systems used to log on to a PC.

When the differences between the two technologies are explained, nearly all users are comfortable with fingerprint technology. The key is the template — what is stored is not a full fingerprint, but a small amount of data derived from the fingerprint's unique patterns.

Response time — AFIS systems may take hours to match a candidate, while fingerprint systems respond within seconds or fractions of a second.

Cost — an AFIS capture device can range from several hundred to tens of thousands of euros, depending on whether it is designed to capture one or multiple fingerprints. A PC peripheral fingerprint device generally costs less than €200-€300.

Accuracy — an AFIS system might return the top 5 candidates in a biometric comparison, with the intent of locating or questioning the top suspects. Fingerprint systems are designed to return a single yes/no answer based on a single comparison.

Scale — AFIS systems are designed to be scalable to thousands and millions of users, conducting constant 1:N searches. Fingerprint systems are almost invariably 1:1 and do not require significant processing power.

Capture — AFIS systems are designed to use the entire fingerprint, rolled from nail to nail, and often capture all ten fingerprints. Fingerprint systems use only the center of the fingerprint, capturing only a small fraction of the overall fingerprint data.

Storage — AFIS systems generally store fingerprint images for expert comparison once a possible match has been located. Fingerprint systems, by and large, do not store images, since they are not used for comparison.

Infrastructure — AFIS systems normally require a backend infrastructure for storage, matching, and duplicate resolution. These systems can cost hundreds of thousands of dollars. Fingerprint systems rely on a PC or a peripheral device for processing and storage.

Already the leading non-AFIS technology in the biometric market, fingerprint is poised to remain the leading non-AFIS technology through 2007. Because of the range of environments in which fingerprint can be deployed, its years of development, and the strong companies involved in the technology's manufacture and development, fingerprint revenues are projected to grow from $144.2m in 2002 to $1,229.8m in 2007. Fingerprint revenues are expected to comprise approximately 30% of the entire biometric market.

A number of basic factors should combine to help drive fingerprint revenues. If and when biometrics become a commonly used solution for e-commerce and remote transactions, segments expected to grow rapidly through 2007, fingerprint will be a primary beneficiary. Fingerprint is a very strong desktop solution, and the desktop is anticipated to become a driver for biometric revenue derived from product sales and transactional authentication. Most middleware solutions leverage a variety of fingerprint solutions for desktop authentication.

Fingerprint is a proven technology capable of high levels of accuracy. The fingerprint has long been recognized as a highly distinctive identifier, and classification, analysis, and study of fingerprints have existed for decades. The combination of an innately distinctive feature with a long history of use as identification sets fingerprint apart in the biometric industry. There are physiological characteristics more distinctive than the fingerprint (the iris and retina, for example), but technology capable of leveraging these characteristics has only been developed over the past few years, not decades.

Strong fingerprint solutions are capable of processing thousands of users without allowing a false match, and can verify nearly 100% of users with one or two placements of a finger. Because of this, many fingerprint technologies can be deployed in applications where either security or convenience is the primary driver.

Reduced size and power requirements, along with fingerprint's resistance to environmental changes such as background lighting and temperature, allow the technology to be deployed across a range of logical and physical access environments. Fingerprint acquisition devices have grown quite small — sensors slightly thicker than a coin, and smaller than 1.5cm x 1.5cm, are capable of acquiring and processing images.

Though radical changes in the composition of the marketplace would need to occur to undermine fingerprint's anticipated growth, the technology does face potential growth inhibitors.

As opposed to technologies such as facial recognition and voice recognition, which can leverage existing acquisition devices, fingerprint's growth is contingent on the widespread incorporation of sensors in keyboards, peripherals, access control devices, and handheld devices. The ability to acquire fingerprints must be present wherever and whenever users want to authenticate. Currently, acquisition devices are present in only a tiny fraction of authentication environments.

A percentage of users, varying by the specific technology and user population, are unable to enroll in many fingerprint systems. Furthermore, certain ethnic and demographic groups have lower-quality fingerprints and are more difficult to enroll. Testing has shown that elderly populations, manual laborers, and some Asian populations are more likely to be unable to enroll in some fingerprint systems. In an enterprise deployment for physical or logical security, this means that some number of users need to be processed by another method, be it another biometric, a password, or a token. In a customer-facing application, this may mean that a customer willing to enroll in a biometric system is simply unable to. In a large-scale 1:N application, the result may be that a user is able to enroll multiple times, as data from their fingerprints cannot be reliably acquired. If the system is designed to be more forgiving and to enroll marginal fingerprints, the common result is increased error rates.

Fingerprint technology is used by hundreds of thousands of people daily to access networks and PCs, enter restricted areas, and authorize transactions. The technology is used broadly across a range of vertical markets and within a range of horizontal applications, primarily PC/network access, physical security/time and attendance, and civil ID. Most deployments are 1:1, though there are a number of "one-to-few" deployments in which individuals are matched against modest databases, typically of 10-100 users. Large-scale 1:N applications, in which a user is identified from a large fingerprint database, are classified as AFIS.

The human fingerprint is comprised of various types of ridge patterns, traditionally classified according to the decades-old Henry system: left loop, right loop, arch, whorl, and tented arch. Loops make up nearly 2/3 of all fingerprints, whorls nearly 1/3, and perhaps 5-10% are arches. These classifications are relevant in many large-scale forensic applications but are rarely used in biometric authentication. This fingerprint is a right loop.

Minutiae, the discontinuities that interrupt the otherwise smooth flow of ridges, are the basis for most fingerprint authentication. Codified in the late 1800s as Galton features, minutiae at their most rudimentary are ridge endings, the points at which a ridge stops, and bifurcations, the points at which one ridge divides into two. Many types of minutiae exist, including dots (very small ridges), islands (ridges slightly longer than dots, occupying a middle space between two temporarily divergent ridges), ponds or lakes (empty spaces between two temporarily divergent ridges), spurs (a notch protruding from a ridge), bridges (small ridges joining two longer adjacent ridges), and crossovers (two ridges that cross each other).

Other features are essential to fingerprint authentication. The core is the inner point, normally in the middle of the print, around which swirls, loops, or arches center. It is frequently characterized by a ridge ending and several sharply curved ridges. Deltas are the points, normally at the lower left and right of the fingerprint, around which a triangular series of ridges centers.

The ridges are also marked by pores, which appear at steady intervals. Some initial attempts have been made to use the location and distribution of pores as a means of authentication, but the resolution required to capture pores consistently is very high.

Once a high-quality image is captured, several steps are required to convert its distinctive features into a compact template. This process, known as feature extraction, is at the core of fingerprint technology. Each of the leading fingerprint vendors has a proprietary feature extraction mechanism; vendors guard these unique algorithms very closely. What follows is a series of steps used, in some form, by many vendors — the basic principles apply even to vendors who use alternative mechanisms.

The image must then be converted to a usable format. If the image is grayscale, areas lighter than a particular threshold are discarded, and those darker are made black. The ridges are then thinned from 5-8 pixels in width down to a single pixel, for precise location of endings and bifurcations.

Minutiae localization begins with this processed image. At this point, even a very precise image will have distortions and false minutiae that need to be filtered out. For example, an algorithm may search the image and eliminate one of two adjacent minutiae, since minutiae are very rarely adjacent. Anomalies caused by scars, sweat, or dirt appear as false minutiae, and algorithms locate any points or patterns that don't make sense, such as a spur on an island (probably false) or a ridge crossing perpendicular to 2-3 others (probably a scar or dirt). A large percentage of would-be minutiae are discarded in this process.

The point at which a ridge ends, and the point where a bifurcation begins, are the most rudimentary minutiae and are used in most applications. There is variance in how exactly to situate a minutia point: whether to place it directly on the end of the ridge, one pixel away from the ending, or one pixel within the ridge ending (the same applies to bifurcation). Once the point has been situated, its location is commonly indicated by its distance from the core, with the core serving as 0,0 on an X,Y axis. Some vendors use the far left and bottom boundaries of the image as the axes, correcting for misplacement by locating and adjusting from the core. In addition to the placement of the minutia, the angle of the minutia is normally used. When a ridge ends, its direction at the point of termination establishes the angle (more complicated rules can apply to curved endings). This angle is taken from a horizontal line extending rightward from the core and can range up to 359 degrees.

In addition to using the location and angle of minutiae, some vendors classify minutiae by type and quality. The advantage of this is that searches can be quicker, as a particularly notable minutia may be distinctive enough to lead to a match. A vendor can also rank high- versus low-quality minutiae and discard the latter. Vendors who shy away from this methodology do so because of the wide variation from print to print, even across successive submissions. Measuring quality may only introduce an unnecessary level of complication.

Approximately 80% of biometric vendors use minutiae in some form. Those who do not use minutiae use pattern matching, which extrapolates data from a particular series of ridges. This series of ridges used in enrollment is the basis of comparison, and verification requires that a segment of the same area be found and compared. The use of multiple ridges reduces dependence on minutiae points, which tend to be affected by wear and tear. The templates created in pattern matching are generally, but not always, 2-3 times larger than in minutiae-based matching — usually 900-1200 bytes.

Form factor is a term used to describe the manner in which a biometric sensor is embedded into an acquisition device. Biometric sensors, in particular fingerprint sensors, can be embedded on top of a device, on its side, recessed, or protruding. Some biometric devices require users to sweep their fingers across the sensor, while others require users to place their fingers on the sensor and hold them still until authenticated.

Though placement of the biometric sensor is important from an ergonomic standpoint, several other considerations are equally important form factors. One of them is the type of device the user interacts with. Several broad categories of device types are listed below.

1. Desktop peripherals. Desktop peripherals include biometrically enabled mice and other handheld devices that computer users interact with when they operate a desktop computer. Because the standard size of desktop peripherals is typically small, the biometric sensor must also be small enough to fit on the device. However, the sensor's ability to acquire images effectively also diminishes as it is made small enough to fit on peripheral devices.
2. Embedded desktop solutions. Embedded desktop solutions include biometrically enabled keyboards and other primary computer components that computer users interact with when they operate a desktop computer. Because embedded desktop devices are larger than desktop peripherals, sensor size is not as significant a consideration — sensors can be large enough to acquire images without compromising the device's ability to operate effectively. Since desktop devices like keyboards are typically cheap, adding an embedded sensor should not significantly increase their cost.
3. Embedded physical access solutions. Embedded physical access solutions include biometrically enabled keypads and other devices that users interact with to gain access to restricted areas (i.e., opening doors). Because physical access solutions are often used to protect items of value, and because making the device small typically isn't a concern, sensors can be large enough to meet this security requirement. Several other factors, including the location of the device (indoors or outdoors), the type of client (military, government, or commercial), and the purpose of the device (apartment access, protecting nuclear materials), will also be important in determining how the embedded physical access solution is deployed.
4. Embedded wireless handheld solutions. Embedded wireless handheld solutions include biometrically enabled cell phones and other mobile personal communication devices that require owner authentication for use. Like desktop peripherals, embedded wireless handheld solutions are small, and consequently the biometric sensor must also be small enough to fit on the device. Similarly, the sensor's ability to acquire images effectively diminishes as it is made small enough to fit on the wireless device.

Ultimately, the type of application being deployed and the environment in which it is being rolled out will drive the form factor. In fact, these form factors have implications for what type of sensor technology is used in the fingerprint device. Today there are three primary sensor technologies: optical, ultrasound, and silicon. Each sensor technology has its advantages and disadvantages. For example, optical sensors are durable and temperature-resistant, qualities that lend themselves well to use in embedded military equipment solutions. However, because optical sensors must be large enough to achieve quality images, they are not well suited to embedded desktop solutions and embedded wireless handheld solutions. On the other hand, silicon sensors are able to produce quality images with less surface area, making them better suited for use in these compact devices.

Acquiring high-quality images of distinctive fingerprint ridges and minutiae is a complicated task. The fingerprint is a small area from which to take measurements, and the wear of daily life affects which ridge patterns show most prominently. Increasingly sophisticated mechanisms have been developed to capture the fingerprint image with sufficient detail and resolution. The technologies in use today are optical, silicon, and ultrasound.

Optical technology is the oldest and most widely used. The finger is placed on a coated platen, usually built of hard plastic but proprietary to each company. In most devices, a charge-coupled device (CCD) converts the image of the fingerprint, with dark ridges and light valleys, into a digital signal. Brightness is either adjusted automatically (preferable) or manually (difficult), resulting in a usable image.

Optical devices have several strengths: they are the most proven over time; they can withstand, to some degree, temperature fluctuations; they are fairly inexpensive; and they can provide resolutions up to 500 dpi. Drawbacks to the technology include size — the platen must be of sufficient size to achieve a quality image — and latent prints. Latent prints are leftover prints from previous users. This can cause image degradation, as severe latent prints can cause two sets of prints to be superimposed. Also, the coating and CCD arrays can wear with age, reducing accuracy.

Optical is the most widely implemented technology, by a significant margin. Identicator and its parent company Identix, two of the most prominent fingerprint companies, use optical technology, much of which is developed jointly with Motorola. The majority of companies use optical technology, but an increasing number of vendors use silicon technology.

Silicon technology has gained considerable acceptance since its introduction in the late 1990s. Most silicon, or chip, technology is based on DC capacitance. The silicon sensor acts as one plate of a capacitor, and the finger is the other. The capacitance between the platen and the finger is converted into an 8-bit grayscale digital image. With the exception of AuthenTec, whose technology employs AC capacitance and reads to the live layer of skin, all silicon fingerprint vendors use a variation of this type of capacitance.

Silicon generally produces better image quality, with less surface area, than optical. Since the chip is comprised of discrete rows and columns — between 200-300 lines in each direction on a 1cm x 1.5cm wafer — it can return exceptionally detailed data. The reduced size of the chip means costs should drop significantly, now that much of the R&D necessary to develop the technology is bearing fruit. Silicon chips are small enough to be integrated into many devices that cannot accommodate optical technology.

Silicon's durability, especially in sub-optimal conditions, has yet to be fully proven. Although manufacturers use coating processes to treat the silicon, and claim the surface is 100x more durable than optical, this remains to be proven. Also, with the reduction in sensor size, it is even more important to ensure that enrollment and verification are done carefully — a poor enrollment may not capture the center of the fingerprint, and subsequent verifications are subject to the same type of placement issue. Many major companies have recently moved into the silicon field. Infineon (the semiconductor division of Siemens) and Sony have developed chips to compete with Veridicom (a spin-off of Lucent), the leader in silicon technology.

Ultrasound technology, though considered perhaps the most accurate of the fingerprint technologies, is not yet widely used. It transmits acoustic waves and measures distance based on the impedance of the finger, the platen, and air. Ultrasound is capable of penetrating dirt and residue on the platen and the finger, countering a main drawback of optical technology.

Until ultrasound technology gains more widespread usage, it will be difficult to assess its long-term performance. However, preliminary usage of products from Ultra-Scan Corporation (USC) indicates that this is a technology with significant promise. It combines a strength of optical technology — large platen size and ease of use — with a strength of silicon technology — the ability to overcome sub-optimal reading conditions.

Legacy links (no longer active) (7)

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• http://www.fingerprints.tk
• http://www.cbdiai.org/Reagents/main.html
• http://www.biometrics.org/REPORTS/SPIE94pore.ps
• http://perso.wanadoo.fr/fingerchip/biometrics/types/fingerprint.htm
• http://www.ams.sunysb.edu/~nkfr/4RESU.HTM
• http://perso.enst.fr/~lunter