So far, this series has provided an overview of physical and behavioral biometric technologies, as well as guidelines about which technology to use in specific situations. In this article we’ll cover the oldest, most tested form of biometric technology: fingerprint recognition.
Fingerprint recognition is the biometric technology that has been around for the longest time, dating back to the 1500’s. Ever since, it has been associated with law enforcement. In fact, the fingerprint has become the de facto standard used in law enforcement.
In fact, fingerprints were used as far back as 2000 BC, used as a legal signature in the primitive business transactions of that time. The first true research paper which attempted to examine the unique structures of the fingerprint was published around 1694, and the very first fingerprint classification system evolved in 1823, by a scientist known as Jan Purkinje. And of course, at the turn of the century, beginning in the early 1900’s, law enforcement agencies here in the United States started to use fingerprints as the primary method to track down known suspects and criminals.
The following statistics illustrate the popularity of using fingerprints as the primary means of verification and/or identification of individuals:
- By 1994, the FBI databases held 810,000 fingerprints.
- By 2003, this number had swelled to more than 200 million fingerprints held in their databases.
- By 2009, this number had escalated to 500 million plus fingerprints held in the FBI databases.
In order to keep up with the growing demand for the use of fingerprints, the FBI devised the Automated Fingerprint Identification System, or AFIS, to automate fingerprint-based searches across national, state, and local law enforcement levels. And to keep up with the growing demands of this gargantuan database, the Integrated Automated Fingerprint Identification System, or IAFIS, was introduced, with enhanced features.
But, in the world of biometrics, the details of the fingerprint are broken down into three distinct levels:
- Level 1: The pattern images which are present in the fingerprint.
- Level 2: The minutiae points of the fingerprint (where most of the unique features are extracted from).
- Level 3: This includes the shapes and the images of the ridges, and the associated pores.
Unique fingerprint features
It is important to note at this point that most of the biometric-based fingerprint systems only collect images at Levels 1 and 2; only the most powerful fingerprint recognition systems collect Level 3 details and are used primarily for identification purposes. The Level 1 specific features include the following:
- Arches: These are the ridges which just flow in one direction, without doubling back, or going backwards. These only comprise about 5% of the features of the fingerprint.
- Loops: In this feature, the ridges go backwards, and go from either the left to the right or the right to the left. There are two distinct types of loops: a) Radial loops which go downward; and b) the ulnar loop which goes upwards on the fingerprint. These make up 65% of the features within the fingerprint.
- Whorls: The ridges in the fingerprint make a circle around a core, and these comprise 30% of the features in the fingerprint.
In addition to the above features, which are collected by a fingerprint recognition system, the actual number of ridges and the way these ridges are positioned (oriented) can also prove to be a very distinctive feature and can contribute to the verification and/or identification of an individual. Other distinctive features which can be extracted, but are less commonly used, include the following:
- Prints/islands: the very short ridges found on a fingerprint
- Lakes: the special indentations/depressions located right in the middle of the ridge
- Spurs: These are the actual crossovers from one ridge to another.
The process of fingerprint recognition
Fingerprint recognition works well not only for verification scenarios, but also for identity applications. This is best illustrated by the gargantuan databases administered by the FBI. But whether it is identification or verification being called for, fingerprint recognition follows a distinct methodology which can be broken down into the following steps:
- The raw images of the fingerprint are acquired through the sensor technology being utilized. At this point, a quality check is also included. This means that the raw images which are collected are eventually examined by the biometric system to see if there is too much extraneous data in the fingerprint image, as this could interfere in the acquisition of unique data. If too much obstruction is found, the fingerprint device will automatically discard that particular image and prompt the end user to place their finger into the platen for another raw image of the fingerprint to be collected. If the raw images are accepted, they are subsequently sent over to the processing unit, which is located within the fingerprint recognition device.
- Once the raw images are accepted by the system, the unique features are then extracted and stored as the enrollment template. If fingerprint recognition is being used by a smartphone, a smart card is then utilized to store the actual enrollment template. Some smartphones can even process the features.
- Once the end user wishes to gain physical or logical access, they must place their finger onto the sensor of the fingerprint recognition system, so that the raw images and unique features can be extracted as described earlier; this becomes the enrollment templates. The enrollment and verification templates are then compared to one another, to determine the degree of similarity/non-similarity with one another.
As you can imagine, the quality control checks which are put into place for the fingerprint raw images are rather extensive. A common error in thinking is that the enrollment and verification templates are the most important in any type of biometric system. While this is true to a certain extent, these templates are actually constructed and created from the raw fingerprint images themselves.
Up Next: The next article in this series will examine the quality control checks that go into fingerprint recognition.
Ravi Das is a Cybersecurity Consultant and Business Development Specialist. He also does Cybersecurity Consulting through his private practice, RaviDas Tech, Inc. He also possesses the Certified in Cybersecurity (CC) cert from the ISC2.
Visit his website at mltechnologies.io
