In this article, we will investigate some of the commonly used fingerprint biometric technologies available in the marketplace. We will then explore the important factors that influence the choice of a biometric solution for 4 different types of applications. In each case, we will examine the level of security needed for the application in comparison with the amount of inconvenience that a specific fingerprint sensor technology causes users.
By Raul Diaz
SELECTING FINGERPRINT BIOMETRICS
Many different fingerprint biometric technologies are available today. The critical decision of which one is right for a particular application is complicated by the tension between security and convenience. A highly secure fingerprint biometrics may be difficult and time-consuming to use. On the other hand, a convenient fingerprint sensor may enhance the ease and speed of use at the expense of security.
The ideal fingerprint sensor would do both. It would identify every person on earth perfectly in any kind of environment including outdoors in the rain, in bright sunlight, in hot and cold weather. This ideal sensor would be small, easy to use, quick and inexpensive. While it is clear that such a perfect fingerprint sensor does not exist, most biometric and security experts would agree that fingerprint technologies, in general, are performing much better today than five or ten years ago.
There are two reasons for this performance improvement:
1) the algorithms that are used to process acquired fingerprint images have improved tremendously; and
2) there is a greater variety of sensors available to obtain reliable fingerprint images in varying applications.
Better fingerprint matching algorithms are used almost universally today to increase performance, but no algorithm can compensate for imperfect or missing images. In fact it is extremely difficult or impossible to obtain reliable fingerprint images from a significant percentage of the world population. There are many reasons that fingerprints may be difficult to acquire, including genetic background, damage from manual labor, poor health, and age. Even environmental conditions can cause severe problems. As I alluded to in my description of the ideal sensor, conditions such as low humidity, high humidity, extreme cold, rain, snow, and direct sunlight negatively affect many fingerprint technologies.
Since an ideal fingerprint sensor for all applications does not exist, we must select a sensor from a wide variety of fingerprint technologies in order to achieve the correct level of security without overly inconveniencing the user.
Multispectral imaging sensor example
(Photo by Lumidigm)
To illustrate the fingerprint biometric sensor selection process, we will present 4 application examples in this article. But before we can select a sensor, we need to understand what fingerprint sensor choices are available and what are their respective advantages and disadvantages. In this discussion, we will cover 3 classes of fingerprint technologies available today: semiconductor, conventional optical and multispectral imaging. This survey is not exhaustive and is meant only to focus the reader on the points to consider when evaluating biometrics for your application.
There are several types of semiconductor fingerprint sensors. The two most common sensors use small silicon arrays to measure the difference in capacitance caused by contact of fingerprint ridges with the sensor or the difference in radio frequency signals returned by the ridges and valleys of the fingerprint. Semiconductor sensors are commonly swipe sensors; the user slides the fingertip over a small sensor area.
Semiconductor swipe sensors are small and inexpensive; they are currently the only reasonable option for portable consumer electronics such as cell phones and laptop computers. However, swipe sensors’ technology inherently limits their suitability for some applications. The silicon array is directly exposed to the environment and is susceptible to damage from mechanical effects, thermal shock, and Electrostatic Discharge (ESD). Capacitive sensors don’t work well when the skin is very dry, and both capacitive and radio frequency sensors fail when the sensor surface is wet. Capacitive sensors measure just the surface features of the skin, causing reduced performance when the features are worn or absent; radio frequency sensors measure features very close (tens of microns) to the surface of the skin making the technology almost as susceptible as capacitive sensors to worn or missing skin features.
Capacitive and radio frequency sensors are very susceptible to spoofs. Spoofs are fake fingerprints, often made of common household materials such as glue or gelatin, that are used to trick the sensor. The simple measurement of the surface characteristics of the skin causes capacitive sensors to be very vulnerable to spoofing. And although spoofing a radio frequency sensor takes a little more sophistication, spoofing this sensor is easy when the proper materials are used.
Finally, because a semiconductor swipe sensor functions as a fingertip scanner, these sensors require user training and practice to work reliably and they frequently fail to capture fingerprint images reliably. These sensors tend to be a poor choice for high-throughput situations or installations that require interactions with unhabituated users.
The size of semiconductor swipe sensors -- such an asset in consumer electronics -- puts them at a disadvantage in large deployments with many users: they simply do not reliably collect enough unique and identifying data about the fingerprint for high biometric performance. Area sensors, in contrast, have a platen upon which the user places a finger. Because they collect more unique fingerprint data, they are inherently better suited to large applications.
Area sensors can be made from semiconductors. However, due to the high cost of semiconductor wafers, these semiconductor area sensors tend to be quite small and, therefore, have limited performance in comparison to optical area sensors. Since semiconductor area sensors suffer from the same direct exposure of the semiconductor to the environment that swipe sensors have, they are similarly fragile. Although semiconductor area sensors are sometimes used for physical access control, they are mainly selected for moderate-to-low security applications such as time and attendance due to their limited biometric performance.
The second sensor class discussed here, conventional optical sensors, are area sensors. They are configured to look for the presence or absence of Total Internal Reflectance (TIR), which is the phenomenon whereby the interface between glass and air acts like a mirror at certain angles. The contact between the skin and the platen defeats (‘frustrates’ the TIR and those points of contact are imaged. Under ideal conditions, the points of contact between the finger and the sensor is the fingerprint ridges and the result is a high-quality image.
Conventional optical fingerprint sensors are generally very robust and much less sensitive to adverse environmental effects such as mechanical shocks or ESD as compared to semiconductor sensors. However, they are very susceptible to non-ideal skin conditions. In particular, if the skin is too dry or is not making good contact with the sensor, the image is severely degraded. The sensors are sensitive to wetness, which destroys the fundamental phenomenon that gives rise to the imaging. They are susceptible to spoofing because information that would differentiate the material in contact with the sensor is not measured.
Multispectral imaging is the most recently developed fingerprint technology available. It is based upon the principle that different wavelengths of light penetrate the skin to different depths and are absorbed differently by various chemical components of the skin. This sensor collects multiple images of the surface and subsurface of the finger under a variety of optical conditions and combines them to yield high-quality and complete fingerprint images. The set of raw multispectral images is also analyzed to ensure that the optical properties of the sample being measured match those expected from a living finger.
The combination of surface and subsurface imaging in these sensors ensures that usable biometric data can be taken across a wide range of environmental and physiological conditions. Bright ambient lighting, wetness, poor contact between the finger and sensor, dry skin, and various topical contaminants present little impediment to collecting usable data. Moreover, the ability of the multispectral imaging sensor to measure the optical characteristics of the skin below the surface allows strong discrimination between living human skin and spoofs. Multispectral imaging sensors are very robust and relatively insensitive to adverse environmental effects such as mechanical shocks or ESD as compared to semiconductor sensors. They are very tolerant of high ambient light levels and are very well suited to industrial and other high-use applications.
EVALUATING THE APPLICATION
Just as important as an understanding of the strengths and weaknesses of fingerprint technologies is an analysis of the application. What is the driving factor in the selection of a technology? It is important to understand the security requirements of your application and the level of convenience needed by the users of the biometric system. The number of people that will use the system will affect both security and convenience. To illustrate this analysis, let us consider specific examples and the decision factors that a security analyst would need to consider in each case. Figure 1. shows the relationship between these factors and highlights the different examples presented in this article.
Example A: Portable consumer electronic device
When it comes to securing personal electronic devices such as personal computers or mobile telephones, cost will be an important consideration. Even more important, however, will be user convenience. If the fingerprint sensor is large or takes a lot of time to use, the end user will not want to purchase the electronic component.
For this application, we can make the following assumptions:
1) The sensor must be small and quick.
2) Very few people will need to use the electronic device (the owner, family members, fellow employees, etc.)
3) The sensor must be very inexpensive.
Semiconductor fingerprint sensors are the best choice for this application, if only because they are the only sensors small enough to do the job. Cost is also a major consideration, and semiconductor sensors are very inexpensive. Operating conditions, usually considered a drawback for semiconductor sensors, are not an issue here since most electronic devices only operate indoors and certainly may not get wet. And since only a small number of people will have access to the device, the sensor does not need to work with large populations.
Technically, since this application has a low number of people using each device, and a moderate False Accept Rate (FAR) is an acceptable security risk. Because a few number of users will be quickly habituated, and because the sensor can be quickly re-swiped in case of a rejection, a moderate False Rejection Rate (FRR) is acceptable. Figure 2 charts the relationship between FAR and FRR. All application examples are shown for reference during these analyses.
Example B: Secure building facility
In this type of application, the overriding concern will be security, and not the convenience of the people using the system. We could make these assumptions for this application:
1) Relatively few, well-trained people are allowed to enter the facility.
2) The sensor must be very accurate.
3) The environment is carefully controlled (office-type environment).
4) The cost of the sensor is not the primary concern.
In a secure building facility application, the environmental and throughput stress on the sensor will be relatively low. The sensor must, however, be able to deliver accurate results. In such an environment, a conventional optical sensor would be a good fit because these sensors provide very accurate images for controlled populations under controlled environments.
Technically, this type of application requires a very low FAR. Security is very high; it should be statistically very difficult for someone who is not registered in the system to enter. A very low FAR usually means that the sensor and matching system must be extremely sensitive to variations and it will deny access to authorized users (higher FRR) from time to time. Convenience is compromised, because authorized users may have to press the sensor repeatedly or more carefully to gain access.
Example C: Theme park ticketing
Theme parks use biometrics for ticket fraud deterrence, a low-security objective. They need to achieve this aim without inconveniencing the customer in any way. The sensor should work quickly, reliably and not cause long lines. For the theme park application, we can make these assumptions:
1) The sensor will likely be outdoors and must work under various environmental conditions.
2) The sensor must work reliably on a very large population with very different skin conditions.
3) The sensor must work very quickly, even at the expense of accuracy.
4) The people using the sensor are not trained at all and may never have used a fingerprint biometric sensor before.
5) The cost of the sensor is not the primary concern.
This application presents great difficulties to the security specialist because very few types of fingerprint sensors are designed to work outdoors. Semiconductor sensors will have extreme difficulty outdoors with rain, humidity, extreme cold and extreme dryness. They will also not tolerate too much contamination of dirt, liquids, grease, etc. Therefore, this type of application would benefit from an area sensor. However, conventional optical sensors do not work well in most outdoor environments either, and they have problems with the high-throughput requirements of this application.
The multispectral imaging technology is currently the sensor of choice at several major theme parks because it is able to function quickly and reliably outdoors under almost any weather conditions. It also works with almost any person in the world. Therefore, for this application, multispectral imaging technology would be the most appropriate.
Technically, this high-convenience application calls for a low FRR because it is important not to reject people very easily. The first time a customer touches the platen, a match should be easily and quickly made. The tradeoff is a relatively higher FAR, but that should be acceptable since fraud deterrence is the primary concern.
Example D: Border control
In this application, we are presented with a very difficult problem. Security must be quite high so that criminals and terrorists or other unauthorized people do not cross the border into a country. Additionally, the application must be very convenient so that a large number of people can be processed relatively quickly. Some border applications also call for outside use, such as Hong Kong, Macau, Singapore and many others. This presents the added stress of varied environmental conditions.
For this application, we may make the following assumptions:
1) The sensor must be very accurate.
2) The sensor should detect false fingerprints.
3) The people using the sensor are not trained well and may not use fingerprint sensors frequently.
4) The sensor must work reliably on a very large population with very different skin conditions.
5) The sensor must work very quickly, with high accuracy to prevent lines at the border.
6) The sensor may be outdoors and must work under various environmental conditions.
7) The cost of the sensor is not the primary concern.
Very few sensors in the world can meet these types of requirements. Historically, conventional optical sensors have been used and they function moderately well. However, increasingly multispectral imaging sensors are being considered for this type of application to handle the reliability concerns of large populations and varied environmental conditions. Moreover, conventional optical sensors cannot detect false fingerprint images very well, whereas multispectral imaging sensors are extremely good at detecting spoofs. In a border control application in which detecting criminals is very important, this ability to detect false fingerprints is critical.
Technically, the security requirements of this application call for a low FAR, but must also have a moderately low FRR to keep the lines short and moving. This means that the sensor must be very accurate and very reliable in all conditions on all people. In the case of FRR situations, a person will be pulled out of line and reviewed manually by a border control agent.
The ideal sensor is perfectly secure and perfectly convenient. In reality, there is a tradeoff between security and convenience. Fortunately, as we have seen, the tradeoff is acceptable in many applications. There are different fingerprint technologies that are a ‘best fit’ for different situations.
One theme underlying the security/convenience analysis is sensor cost. This article does not directly address the issue of price point. However, the cost of the sensor is generally correlated with its fundamental biometric performance. This correlation is not always linear, and in some cases, new technologies can displace previous biometric sensors with a new price/performance level. At the time of writing, semiconductor sensors are considerably less expensive than conventional optical sensors, which are generally less expensive than multispectral imaging sensors. Conversely, multispectral imaging sensors provide the highest level of performance with semiconductor sensors generally toward the lower end of the performance curve and conventional optical sensors somewhere in the middle. As the biometrics market is continuous to mature, we expect to see continuing price declines, and relatively new technologies, such as multispectral imaging, are likely to see large price declines since they are just entering the market in volume applications.
We hope that this overview of fingerprint biometric technologies helps you in your decision to select the most appropriate biometric for your application. As we have stated, this is not an exhaustive survey. Rather, this article presents a methodology with which to compare fingerprint biometric sensors against each other and to gauge their fitness for your security application.
Raul Diaz is Vice President of Sales and Marketing for Lumidigm (www.lumidigm.com).
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