Fingerprint Classifications Practical Applications Term Paper

Pages: 9 (2481 words)  ·  Bibliography Sources: ≈ 5  ·  File: .docx  ·  Level: College Senior  ·  Topic: Criminal Justice

Arches do not have type lines, deltas, or cores.

Type lines are two diverging ridges usually coming into and splitting around an obstruction, such as a loop. A delta is the ridge point nearest the type line divergence. The core is the approximate center of the pattern. A loop must have one or more ridges entering from one side of the print, recurving, and exiting from the same side. If a loop opens toward the little finger, it is called an ulnar loop. If it opens toward the thumb, it is a radial loop. The patterned area of any loop is surrounded by two type lines. All loops must have one delta (Saatci & Tavsanoglu, 2003).

All whorl patterns must have type lines and a minimum of two deltas. A plain whorl and central pocket loop have at least one ridge that makes a complete circuit. This ridge may be in the form of a spiral, an oval, or any variant of a circular form. The main difference between these two patterns can be shown if an imaginary line is drawn between the two deltas contained within the two patterns. If the line touches any one of the spiral ridges, the pattern is determined to be a plain whorl, if no ridge is touched, the pattern is a central pocket loop. The double loop is made up of any two loops combined into one fingerprint. Any print classified as accidental either contains two or more patterns or the pattern is not covered by other categories, i.e. A combination loop and a plain whorl or a loop and tented arch.Buy full Download Microsoft Word File paper
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Once a high-quality image is captured, there are a several steps required to convert its distinctive features into a compact template. This process, known as feature extraction, is at the core of fingerprint technology. The image must be converted to a usable format. If the image is grayscale, the areas lighter than a particular threshold are discarded, and those areas darker are made black. The ridges are then thinned from five to eight pixels in width down to one pixel, for precise location of the endings and bifurcations. Minutiae localization begins with this processed image. At this point, even a very precise image will have distortions and false minutiae that need to be filtered out. For example, an algorithm may search the image and eliminate one of two adjacent minutiae, as minutiae are very rarely adjacent. Anomalies caused by scars, sweat, or dirt may occasionally appear as false minutiae, and algorithms have the ability to locate any points or patterns that don't make sense, such as a spur on an island or a ridge crossing perpendicular to others. A large percentage of possible minutiae are typically discarded in this process.

The point at which a ridge ends, and the point where a bifurcation begins, are the most rudimentary minutiae, and are used in most applications. There is variance in how exactly to situate a minutia point. One may place it directly on the end of the ridge, one pixel away from the ending, or one pixel within the ridge ending. The same concept also applies to bifurcation. Once the point has been situated, its location is commonly indicated by the distance from the core, with the core serving as the 0,0 on an X, Y-axis. Some vendors use the far left and bottom boundaries of the image as the axes, correcting for misplacement by locating and adjusting from the core. In addition to the placement of the minutia, the angle of the minutia is normally used. When a ridge ends, its direction at the point of termination establishes the angle. This angle is taken from a horizontal line extending rightward from the core, and may be up to 359 degrees.

In addition to using the location and angle of minutiae, minutia may be classified by type and quality. The advantage of this is that searches can be quicker, as a particularly notable minutia may be distinctive enough to lead to a match. Approximately 80% of biometric vendors typically utilize minutiae in some fashion (Azoury et al., 2004). Those who do not utilize minutia use pattern matching, which is a technique that extrapolates data from a particular series of ridges. This series of ridges used in enrollment is the basis of comparison, and verification requires that a segment of the same area be found and compared. The use of multiple ridges reduces dependence on minutiae points, which tend to be affected by wear and tear. The templates created in pattern matching are generally, but not always, two to three times larger than in minutia, which are usually 900-1200 bytes.

In summary, classification of fingerprints has been used to identify individuals for over a century. This classification is a very sophisticated task that has numerous practical applications in day-to-day activities. Advanced algorithms filter data that contain information about specific anatomic landmarks and match these data to fingerprints of individuals saved in databases. The accuracy of this technology is extremely high. Future technologies such as DNA fingerprinting and iris detection algorithms may outdate fingerprinting as the gold-standard individual identification modality.


Azoury, M., Cohen, D., Himberg, K., Qvintus-Leino, P., Saari, T., & Almog, J. (2004). Fingerprint detection on counterfeit U.S. dollar banknotes: the importance of preliminary paper examination. J Forensic Sci, 49(5), 1015-1017.

Blotta, E., & Moler, E. (2004). Fingerprint image enhancement by differential hysteresis processing. Forensic Sci Int, 141(2-3), 109-113.

Maudling, N., & Attwood, T.K. (2004). FAN: fingerprint analysis of nucleotide sequences. Nucleic Acids Res, 32(Web Server issue), W620-623.

Saatci, E., & Tavsanoglu, V. (2003). Fingerprint image enhancement using CNN filtering techniques. Int J. Neural Syst, 13(6), 453-460.

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