Biometric sensors are devices that can collect information capable of performing an automatic determination of a person's identity. Common types of biometric technologies include facial recognition, iris recognition, and fingerprint sensing. Although not yet ubiquitous, biometric sensors—and particularly fingerprint sensors—are poised to become much more prevalent in many people's lives for both commercial and government applications.
Recent commercial introductions of fingerprint technology include an IBM laptop computer with a built-in fingerprint sensor, and a line of fingerprint-enabled computer products from Microsoft including a keyboard and mouse. The goal of both products is to offer an alternative to user names and passwords as a means to authorize access to computers, networks, and services.
In the aftermath of 9/11, the federal government and other governing bodies around the world have turned to biometrics to verify the identity of foreign travelers entering and leaving the U.S. and other countries. Furthermore, as networked digital devices become more prevalent in warfare, there is a growing movement toward biometric sensing to ensure that the person using a particular device is authorized to do so. Fingerprint sensors are among the leading candidate technologies for these and many other security applications.
Conventional fingerprint sensors read the superficial friction ridge patterns of the skin on the fingertips. Common sensor types include capacitive, radio frequency, thermal, and optical arrays. Although each sensing modality is fundamentally different from the others, each generates an image that distinguishes between points of contact with the sensor (fingerprint ridges) and points where there is a gap (fingerprint valleys).
A common problem with current fingerprint sensors is that they are an easy target for "spoofing"—the art of using artificial samples to imitate real and authorized fingerprint patterns. While this security breach is a concern for all biometric technologies, the problem is particularly pronounced for fingerprint sensors because people leave copies of their fingerprints on most objects they touch throughout the day. A quick search on the Internet can provide a motivated individual with enough information to convert a latent fingerprint into an effective spoof using familiar materials that can be bought at a hobbyist supply shop.
The reason most fingerprint sensors are susceptible to spoofing is that little or no information is acquired specific to the object touching the sensor. As long as the sensor can produce a pattern sufficiently close to that of the enrolled fingerprint, authorization is granted. As an example, consider a common type of optical fingerprint sensor based on total internal reflectance (TIR), as shown in Figure 1. An image is formed when a material with an appropriate index of refraction contacts the sensor surface. This material can be human skin, but it can also be silicone, gelatin, or a variety of other substances.
 Figure 1. In a typical optical fingerprint sensor based on total internal reflectance (TIR), light enters from the left of the prism and is diffusely reflected from the right-side facet to uniformly illuminate the sample surface. The imager detects bright regions where air gaps are located and darker areas where skin or other material of appropriate refractive index is in contact with the glass (inset). |
Furthermore, superficial fingerprint patterns may be worn, damaged, or simply hard to read due to the skin's surface conditions—too wet or too dry, for example. Collecting fingerprints of good quality from older people is particularly difficult because their skin is likely not to be very supple. The resulting poor-quality images, even if collected from a properly authorized person, can lead to authorization rejections. The system administrator might then set the sensor's security threshold to a more lenient setting to reduce the number of false negatives. This relaxed threshold further exacerbates the susceptibility of a sensor to spoof attacks.
A very powerful way to address these security concerns is by collecting a multispectral image of the skin directly below the surface fingerprint. Living human skin has certain unique optical characteristics due to its chemical composition, which predominately affects optical absorbance properties, as well as its multilayered structure, which has a significant effect on the resulting scattering properties. By collecting images generated from different illumination wavelengths passed into the skin, different subsurface skin features may be measured and used to ensure that the material is living human skin. When such a multispectral sensor is combined with a conventional fingerprint reader, the resulting sensing system can provide a high level of certainty that the fingerprint originates from a living finger.
The key components of a multispectral imager suitable for imaging fingers are shown in Figure 2 The light sources are LEDs of various wavelengths spanning the visible and short-wave IR region. Crossed linear polarizers may be included in the system to reduce the contribution of light that undergoes a simple specular reflection to the image, such as light that is reflected from the surface of the skin. The crossed polarizers ensure that the majority of light seen by the imaging array has passed through a portion of skin and undergone a sufficient number of scattering events to have randomized the polarization. The imaging array is a common silicon CMOS or CCD detector.
 Figure 2. The key components of a multispectral imager include illumination sources (LEDs of various wavelengths), an imaging array (silicon CCD or CMOS), and a crossed polarizer arrangement that emphasizes light that has undergone multiple scatter events in the skin. |
In general, the optical resolution requirements for this application of a multispectral imager are relatively modest. Because of the highly scattering nature of skin, the multispectral imager need not have a greater resolution than that used for the conventional fingerprint image, typically 250–1000 pixels/in. Assuming a nominal 1 in. sensing surface, a readily available VGA or 1.3 megapixel array provides adequate resolution for most applications.
Figure 3 shows the layout of an optical fingerprint sensor that combines a conventional total internal reflection (TIR) fingerprint reader with a multispectral imager. As can be seen, both sensors can view a finger placed on the sensing surface without interfering with each other. The multispectral imager can thus provide significant new biometric information without requiring any different or additional actions on the part of the user.
 Figure 3. This conceptual layout illustrates an optical fingerprint sensor that combines a conventional TIR-based imager and a multispectral imager. Both can view a finger simultaneously. |
An example of a conventional fingerprint image and a multispectral image of the same finger is illustrated in Figure 4. In this case, the skin of the subject's finger is relatively dry, causing a noticeable deterioration in the contrast and continuity of the lines in the conventional fingerprint image. In contrast, the multispectral pseudocolor image shows spectral and spatial features that are well defined and consistent with a living finger. In addition, the fingerprint image is observable in the multispectral data, which can be used to further authenticate the conventionally collected fingerprint pattern as well as to augment missing or poorly defined portions of the conventional fingerprint.
 Figure 4. The print on the left of a dry fingertip was taken with a conventional TIR imager. On the right is the same finger, but imaged with a multispectral sensor. The latter image was collected using five wavelengths (475, 500, 560, 576, and 625 nm). It is shown here as a pseudo-color representation of the first three factors produced by a decorrelation-stretching technique operating on the original five image planes. |
A highly realistic artificial finger made by Alatheia Prosthetics (Brandon, MS) was one of a number of different spoof samples used to test a multispectral imager's ability to discriminate between real fingers and spoofs. Figure 5 shows the results of a multivariate spectral discrimination performed to compare the consistency of the spectral content of a multispectral image of a real finger with both a second image of a real finger and a prosthetic replica of the same finger. The imager's ability to distinguish between the two sample types is clear.
 Figure 5. Multispectral image data can clearly discriminate between a living finger and an ultra-realistic spoof. The graphs on the left show how similar the spectral content of each image is to that expected for a genuine finger. |
Lumidigm is currently working with partners to integrate the multispectral technology into conventional optical fingerprint sensors. The resulting product is scheduled to become available later this year for applications including Homeland Security and commercial physical access. One anticipated benefit is a level of security and usability that goes far beyond today's fingerprint sensors and that will pave the way to broader adoption of fingerprint sensors as the biometric of choice.
This material is based on work supported by the Air Force Research Laboratory, Rome, NY, under contract number FA8750-04-C-0190. Any opinions, findings, conclusions, or recommendations expressed herein are those of the author and do not necessarily reflect the views of the Air Force Research Laboratory.
Robert K. Rowe, Ph.D., is Chief Technology Officer, Lumidigm, Inc., Albuquerque, NM; 505-272-7406, [email protected], www.lumidigm.com.