RootTechnologyFingerprint Technology

About fingerprint recognition

Ridge Bifurcation
Ridge Ending

Fingerprint recognition represents the oldest method of biometric identification, its history is going back as far as at least 6000 BC. The use of fingerprints as a personal code has a long tradition and was already used by the Assyrians, the Babylonians, the Chinese and the Japanese. Since 1897, dactyloscopy (synonym for non-computer-based fingerprint identification) has been used for criminal identification.

A fingerprint consists of ridges (lines across fingerprints) and valleys (spaces between ridges). The pattern of the ridges and valleys is unique for each individual. There are two major methods of fingerprint matching: Minutiae matching and global pattern matching. The first approach analyses ridge bifurcations and endings, the second method represents a more macroscopic approach, considering the flow of ridges in terms of, for example, arches, loops and whorls.

The equal-error-rate is low, therefore fingerprint recognition is very accurate. The prices of such systems compared to other biometric systems are quite low and also the user acceptance is very high.

Fingerprint-based identification can be placed into two categories: Minutiae-based matching (analysing the local structure) and global pattern matching (analysing the global structure). Currently the computer aided fingerprint recognition is using the minutiae-based matching. Minutiae points are local ridge characteristics that appear as either a ridge ending or a ridge bifurcation.

The uniqueness of a fingerprint can be determined by the pattern of the ridges and the valleys a fingerprint is made of. A complete fingerprint consists of about 100 minutiae points in average. The measured fingerprint-area consists in average of about 30-60 minutiae points depending on the finger and on the sensor area.

These minutiae points are represented by a cloud of dots in a coordinate system. They are stored together with the angle of the tangent of a local minutiae point in a fingerprint-code or directly in a reference template. A template can consist of more than one fingerprint-code to expand the amount of information and to expand the enrolled fingerprint area. In general this leads to a higher template quality and therefore to a higher similarity value of the template and the sample.

The template sizes varies from 100 bytes to 1500 Bytes depending on the algorithm and the quality of a fingerprint. Nevertheless, very rarely there are fingerprints without any minutiae-points that leads to a failure to enroll (FER = Failure to Enroll Rate). It is also difficult to extract the minutiae points accurately when the fingerprint has got a low quality.

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