In the Western countries, fingerprint authentication is the norm for most of the basic authentication process. Due to its uniqueness, fingerprint authentication has been the most sought for commercial applications. This article takes a look at the technical aspects of fingerprint authentication along with the matching algorithms.

Fingerprint identification is one of the most well-known and publicized biometrics. Because of their uniqueness and consistency over time, fingerprints have been used for identification for over a century, more recently becoming automated (biometric) due to advancements in computing capabilities. Fingerprint identification is popular because of the inherent ease in acquisition, the numerous sources (ten fingers) available for collection, and their established use and collections by law enforcement and immigration.

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.

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. It is also necessary to know the structure and properties of human skin in order to successfully employ some of the imaging technologies.

The Uniqueness

Extensive research has been done on fingerprints in humans. Two of the fundamentally important conclusions that have risen from research are:

1) A person's fingerprint will not naturally change structure after about one year after birth and
2) the fingerprints of individuals are unique. Even the fingerprints in twins are not the same. In practice two humans with the same fingerprint have never been found.

The Basics

A fingerprint is comprised of ridges and valleys. The ridges are the dark area of the fingerprint and the valleys are the white area that exists between the ridges. Many classifications are given to patterns that can arise in the ridges and some examples are given in the figure to the right. These points are also known as the minutiae of the fingerprint. The most commonly used minutiae in current fingerprint recognition technologies are ridge endings and bifurcations because they can be easily detected by only looking at points that surround them.

Hardware Requirements

In order to analyze the fingerprints, a variety of sensor types - optical, capacitive, ultrasound, and thermal are used for collecting the digital image of a fingerprint surface. Optical sensors take an image of the fingerprint, and are the most common sensor today. The capacitive sensor determines each pixel value based on the capacitance measured, made possible because an area of air (valley) has significantly less capacitance than an area of finger (friction ridge skin). Other fingerprint sensors capture images by employing high frequency ultrasound or optical devices that use prisms to detect the change in light reflectance related to the fingerprint. Thermal scanners require a swipe of a finger across a surface to measure the difference in temperature over time to create a digital image.

Scanning a Fingerprint

Many of the algorithms require a linear scan of the fingerprint image. Scanning is achieved by moving a fixed size window across the picture in a grid-like pattern. This can be seen in the image to the right. However, it is possible that areas of interest do not lie squarely in the one of the windows. To account for this the window is then shifted just vertically, just horizontally, and then vertically and horizontally by half the window size and the grid scan is completed again. Therefore, it takes four scans of the image to do the linear scan. This is not a problem because it is used for the preprocessing of a fingerprint image (which only occurs once) and is done in a linear manner.

Classes of Algorithms

The two main categories of fingerprint matching techniques are minutiae-based matching and pattern matching. Pattern matching simply compares two images to see how similar they are. Pattern matching is usually used in fingerprint systems to detect duplicates. The most widely used recognition technique, minutiae-based matching, relies on the minutiae points described below, specifically the location and direction of each point.

Let us discuss the two main categories of fingerprint matching techniques in brief in the following sections

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.

Illustration 1 : The Arch Pattern.

Illustration 2 : The Loop Pattern.

Illustration 3 : The Whorl Pattern.

Minutia 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.

Illustration 4 : Ridge ending.

Illustration 5 : Bifurcation.

Illustration 6 : Short Ridge (Dot).

Matching Algorithms

Fingerprint matching is the process used to determine whether two sets of fingerprint ridge detail come from the same finger. There exist multiple algorithms that do fingerprint matching in many different ways. Some methods involve matching minutiae points between the two images, while others look for similarities in the bigger structure of the fingerprint.

Fingerprint 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.

Pattern-based Algorithms

Pattern based or image-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.

Minutiae Matching

Most modern fingerprint matching technologies use minutiae matching. The idea being if you can find enough minutiae in one image that have corresponding minutiae in another image then the images are most likely from the same fingerprint. Minutiae are usually matched together by their distance relative to other minutiae around it. If multiple points in one image have similar distances between them then multiple points in another image then the points are said to match up. It is the idea of this paper to add the constraint that the regions and possibly edges between the minutiae should be the approximately the same as well.

A Brief Conclusion

For over a century, fingerprints have been one of the most highly used methods for human recognition; automated biometric systems have only been available in recent years. The determination and commitment of the fingerprint industry, government evaluations and needs, and organized standards bodies have led to the next generation of fingerprint recognition, which promises faster and higher quality acquisition devices to produce higher accuracy and more reliability. Because fingerprints have a generally broad acceptance with the general public, law enforcement, and the forensic science community, they will continue to be used with many governments’ legacy systems and will be utilized in new systems for evolving applications that require a reliable biometric.

—By:R. Manoj, The author is an Assistant Editor at Fanatic Media, Bangalore. He is also an Independent Researcher, specializing in Systems Security. He has an active interest in designing security algorithms for securing mission critical systems. He can reached at infosecurity@fanaticmedia.com