Fingerprint recognition

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 an individual and verify their identity. This article touches on two major classes of algorithms (minutia and pattern) and four sensor designs (optical, ultrasonic, passive capacitance, and active capacitance).

ANALYSIS

      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.[1]  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 are inherited. [2]

The arch pattern.

The loop pattern.

File:Whorl.jpg The whorl pattern.

MINUTIAE FEATURES

       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.[3]

Ridge ending.

File:Bifurcation.JPG Bifurcation.

Short Ridge (Dot).

FINGERPRINT SENSORS

          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.[4]

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.^[5]

Capacitance

Capacitance sensors utilize the principles associated with capacitance in order to form fingerprint images. The two equations used in this type of imaging are:

$C={\frac {Q}{V}}$

$C=\epsilon _{0}\epsilon _{r}{\frac {A}{d}}$

where

C is the capacitance in farads

Q is the charge in coulombs

V is the potential in volts

ε₀ is the permittivity of free space, measured in farad per metre

ε_r is the dielectric constant of the insulator used

A is the area of each plane electrode, measured in square metres

d is the separation between the electrodes, measured in metres

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.^[6]

Active capacitance

Active capacitance sensors use a charging cycle to apply a voltage to the skin before measurement takes place. The application of voltage charges the effective capacitor. The electric field between the finger and sensor follows the pattern of the ridges in the dermal skin layer. On the discharge cycle, the voltage across the dermal layer and sensing element is compared against a reference voltage in order to calculate the capacitance. The distance values are then calculated mathematically, using the above equations, and used to form an image of the fingerprint.^[7] Active capacitance sensors measure the ridge patterns of the dermal layer like the ultrasonic method. Again, this eliminates the need for clean, undamaged epidermal skin and a clean sensing surface.^[8]

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.^[9]

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.^[10]

Minutia-based algorithms

Minutia-based algorithms compare several minutia points (ridge ending, bifurcation, and short ridge) extracted from the original image stored in a template with those extracted from a candidate fingerprint. Similar to the pattern-based algorithm, the minutia-based algorithm must align a fingerprint image before extracting feature points. This alignment must be performed so that there is a frame of reference. For each minutia point, a vector is stored into the template in the form:

$m_{i}=(type,x_{i},y_{i},\theta _{i},W)$

where

m_i is the minutia vector

type is the type of feature (ridge ending, bifurcation, short ridge)

x_i is the x-coordinate of the location

y_i is the y-coordinate of the location

θ_i is the angle of orientation of the minutia

W is a weight based on the quality of the image at that location

It is important to note that an actual image of the print is not stored as a template under this scheme. Before the matching process begins, the candidate image must be aligned with the template coordinates and rotation. Features from the candidate image are then extracted and compared with the information in the template. Depending on the size of the input image, there can be 10-100 minutia points in a template. A successful match typically only requires 7-20 points to match between the two fingerprints.^[11]

See also

• Automated Fingerprint identification
• Fingerprint Verification Competition
• Biometrics
• Authentication
• Fingerprint
• Minutiae
• Feature extraction
• Skin
• Heredity
• Medical ultrasonography
• Piezoelectricity
• Algorithm

References

1. ^ Jain, L.C. et al. (Eds.). 1999. ‘’Intelligent Biometric Techniques in Fingerprint and Face Recognition.’’ Boca Raton, FL: CRC Press.
2. ^ Johnson, B. (2005, August 29), lecture presented in Electrical and Computer Engineering 586, University of Virginia, Charlottesville, VA. Retrieved December 13, 2005 from https://toolkit.itc.virginia.edu/cgi-local/tk/UVa_SEAS_2005_Fall_ECE586-1/displaymaterials:LectureSlides+Lecture-2.ppt/SESSION:113453174811608:44402702625757/Lecture-2.ppt
3. ^ Johnson, B. (2005, August 31), lecture presented in Electrical and Computer Engineering 586, University of Virginia, Charlottesville, VA. Retrieved December 13, 2005 from https://toolkit.itc.virginia.edu/cgi-local/tk/UVa_SEAS_2005_Fall_ECE586-1/displaymaterials:LectureSlides+Lecture-4.ppt/SESSION:113453174811608:44402702625757/Lecture-4.ppt
4. ^ Johnson, B. (2005, September 12), lecture presented in Electrical and Computer Engineering 586, University of Virginia, Charlottesville, VA. Retrieved December 13, 2005 from https://toolkit.itc.virginia.edu/cgi-local/tk/UVa_SEAS_2005_Fall_ECE586-1/displaymaterials:LectureSlides+Lecture-5.ppt/SESSION:113453174811608:44402702625757/Lecture-5.ppt
5. ^ Minutia vs. Pattern Based Fingerprint Templates. (2003). Retrieved December 13, 2005, from http://www.ibia.org/membersadmin/whitepapers/pdf/9/M_vs_P_White%20Paper_v2.pdf
6. ^ Setlak, D.R. ‘’Advances in Biometric Fingerprint Technology are Driving Rapid Adoption in Consumer Marketplace.’’ Retrieved December 13, 2005 from http://www.authentec.com/getpage.cfm?sectionID=43