Classification of fingerprints
Fingerprints are classified according to the characteristics of the ridges in relation to the nucleus and its delta(s). Thus we find the following types of fingerprints:
- Arches: This type of tracks are characterized because they do not have a delta, while the crests form arches that do not return on themselves.
- Presilla: These are fingerprints that have the delta on the left or right side of the nucleus, while the crests that arise from the nucleus move in the opposite direction to the delta.
- Whorls: In these fingerprints there are two deltas, one on each side of the nucleus. While the nucleus adopts a circular or spiral shape.
- Others: They are fingerprints with different patterns that do not fit in the above classification.
Reasons to use fingerprints
Why choose fingerprints instead of other biometric systems such as face or retina to identify people?
- Fingerprint recognition technology has a low probability of error, approximately 1 in 100 million.
- It is a technology that has been used in the world for more than 100 years, with great success by police agencies in criminal cases.
- Finally, fingerprint biometrics can be easily massified, with low implementation costs in its lighter versions, being accurate and non-intrusive in terms of measuring human physical characteristics.
Fingerprint quality
For a fingerprint to be considered of good quality, the following aspects must be considered:
- The core must be very clearly identified.
- There should be no deformations in the ridges.
- The fingerprint taken must contain at least 63 minutiae.
These examples show deformations in the ridges.
In these other examples, the core of the print cannot be clearly identified:
Failure to identify the nucleus means that a fingerprint cannot be characterized in the mathematical model:
In this type of poor quality prints, where there are areas that are not clear, where the core is not well recognized, the systems easily recognize false minutiae:
When we have good quality images of footprints, the nucleus is clearly identified, the ridges are sharp, and the minutiae are easily identified.
A good quality fingerprint is then identified by the position of the nucleus and all its minutiae, which are converted into a mathematical model, which by numerically representing the fingerprint in terms of its most important characteristics, allows easy comparison with other fingerprints in the matching process. This means that the comparison of fingerprints is not made between images, but between the mathematical models that represent it.
This matching of fingerprints can be found in two ways:
- 1:1 : When a fingerprint of a particular finger of a person is compared with the stored fingerprint of that same finger of the same person. This is a quick and easy process.
- 1:N: In this case the matching is done between a captured fingerprint with all the stored fingerprints of many individuals. This process is used to find if a person is not registered in a system, or in a forensic process.
Types of fingerprint scanners
In this section we will describe some types of fingerprint readers available on the market: