Fingerprint Biometrics

A fingerprint is made of a a number of ridges and valleys on the surface of the finger. Ridges are the upper skin layer segments of the finger and valleys are the lower segments. The ridges form so-called minutia points: ridge endings (where a ridge end) and ridge bifurcations (where a ridge splits in 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 which cross each other). The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points. There are five basic fingerprint patterns: arch, tented arch, left loop, right loop and whorl. Loops make up 60% of all fingerprints, whorls account for 30%, and arches for 10%. Fingerprints are usually considered to be unique, with no two fingers having the exact same dermal ridge characteristics.

How does fingerprint biometrics work

The main technologies used to capture the fingerprint image with sufficient detail are optical, silicon, and ultrasound. There are two main algorithm families to recognize fingerprints:

• Minutia matching compares specific details within the fingerprint ridges. At registration (also called enrollment), the minutia points are located, together with their relative positions to each other and their directions. At the matching stage, the fingerprint image is processed to extract its minutia points, which are then compared with the registered template.
• Pattern matching compares the overall characteristics of the fingerprints, not only individual points. Fingerprint characteristics can include sub-areas of certain interest including ridge thickness, curvature, or density. During enrollment, small sections of the fingerprint and their relative distances are extracted from the fingerprint. Areas of interest are the area around a minutia point, areas with low curvature radius, and areas with unusual combinations of ridges.

Issues with fingerprint systems

The tip of the finger is a small area from which to take measurements, and ridge patterns can be affected by cuts, dirt, or even wear and tear. Acquiring high-quality images of distinctive fingerprint ridges and minutiae is complicated task. People with no or few minutia points (surgeons as they often wash their hands with strong detergents, builders, people with special skin conditions) cannot enroll or use the system. The number of minutia points can be a limiting factor for security of the algorithm. Results can also be confused by false minutia points (areas of obfuscation that appear due to low-quality enrollment, imaging, or fingerprint ridge detail). Note: There is some controversy over the uniqueness of fingerprints. The quality of partial prints is however the limiting factor. As the number of defining points of the fingerprint become smaller, the degree of certainty of identity declines. There have been a few well-documented cases of people being wrongly accused on the basis of partial fingerprints.

Benefits of fingerprint biometric systems

• Easy to use
• Cheap
• Small size
• Low power
• Non-intrusive
• Large database already available

Applications of fingerprint biometrics

Fingerprint sensors are best for devices such as cell phones, USB flash drives, notebook computers and other applications where price, size, cost and low power are key requirements. Fingerprint biometric systems are also used for law enforcement, background searches to screen job applicants, healthcare and welfare.

Fingerprint Identification Systems

Fingerprint Recognition: Strengths Most widely used technology Proven technology capable of high accuracy Ability to enroll multiple fingers Wide range of deployment environments	Fingerprint Recognition:Considerations perception of law enforcement, forensic uses Impaired or damaged fingerprints May Require additional hardware and software Standards needed for interoperability
Iris recognition: Strengths Highly reliable, hands-free operation High stability of characteristics over lifetime Iris is a rich source of biometric data Successful tests in air travel	Iris recognition: Considerations Acquisition of iris image requires more training and attentiveness than most biometrics Hardware and software licensing costs Glasses with strong lenses may impact performance Potential for false non-matching
Hand Geometry: Strengths Able to operate in challenging environments Established, reliable core technology Perceived as non-intrusive	Hand Geometry: Considerations Design complicates usage by certain population Perception of bio-hazard, passing gems Possible hand changes over time
Face Recognition: Strengths May operate without user compliance Leverage existing image databases Only technology capable of identification at a distance and surveillance	Face Recognition: Considerations Susceptible to high false match rates in one-to-one and one-to-many applications Lighting, camera angle reduce matching accuracy Changes in physiological characteristics reduce matching accuracy

Technology Characteristic	Fingerprint	Iris	Facial	Hand
How it works	Capture and compares fingertip s patterns	Captures and compares iris patterns	Captures and compares facial patterns	Measures and compare dimensions of hand and fingers
cost of device	Low	High	Moderate	Moderate
Enrollment time	About 3 minutes, 30 seconds	2 minutes, 15 seconds	About 3 minutes	About 1 minute
Transaction timea	9 to 19 seconds	12 seconds	10 seconds	6 to 10 seconds
False nonmatch rateb	.2%-36%	1.9%-6%	3.3%-70%	0%-5%
False match rate(FMR)c	0%to8%	Less than 1%	0.3%-5%	0%-2.1%
User acceptance issues	Associated with law enforcement, hygiene concerns	User resistance, usage difficulty	Potential for privacy misuse	Hygiene concerns
factors affecting Performanced	Dirty, dry or worn fingertips	Poor eyesight, glare, or reflection	Lighting, Orientation of face and sunglasses	Hand injuries, arthritis, swelling
Demonstrated vulnerabilitye	Artificial fingers, reactivated latent prints	High-resolution picture of iris	Notebook computer with digital photographs	None
Variability with agesf	Stable	Stable	Affected by aging	Stable
Commercial availability since	1970s	1997	1990s	1970s

1. ^(a) Amount of time it takes to verify machine-red biometric versus stored biometric.
2. ^(b) The probability that individuals who should be matched are not matched by a biometrics system.
3. ^(c) The probalility of an erroneous match in a singgle template comparison.
4. ^(d) Human characteristics or measurement condition circumstances that could adversely affect accuracy of biometric systems.
5. ^(e) Demonstrated methods of beating biometric systems that have benn employed in tests.
6. ^(f) Effects of age, if any, of individual on his or her biometric identifiers

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