Fingerprint scientific method - Coursework Example

As we can see that every single person in this world differs from one another by face likewise scientists have devised other ways for identifying each person by using different methods. Of which one method is analyzing an individual’s fingerprints. Fingerprints are often used by police as a forensic investigatory tool to reach the target culprit. Scientifically it is proved that every individual possesses dissimilar shades or lines on fingers and that pattern of which never changes and remains the same till death. 1. The Scientific Method An algorithm called The Universal Numeric Fingerprints (UNFs) is used to validate the data or digital object matrices that it has been successfully & precisely transformed/decoded when moved from one software/format to another.

Whenever, any update or addition is required in the original data, the software creates a new UNF each time therefore UNF is based on the data content and not on the data format. It comprises 3 steps to compute a fingerprint. No. 1 – an approximation algorithm is used to compute a reduced-fidelity version of the target digital object. No. 2 – this reduced version is put into a normalized serial form, and No. 3 – a hash function is used to compute a unique fingerprint from the resulting serialized object.

The UNFs approach is somewhat similar to that of audio & video fingerprinting. However, in contrast to audio and video fingerprints, which entail a long sequence computation, UNFs use a more concise and closely related representation, suitable for use in scholarly citations. Thai, L. & Tam, H. (2010) under ‘Standardized fingerprint model’ states, “The model includes the following steps: (1) Pre-processing fingerprint image: for each image, we recognize fingerprint area, thinned ridge lines and extract minutiae. (2) Finding and adjusting parameter sets: at first, choose a fingerprint which has largest fingerprint area as mean image.

Then, we use Genetic Algorithms in [9] to find the transformation between mean image and others. (3) Synthesizing fingerprint: with the transformations in previous step, we re-calculate parameters’ value (to get exact value for parameters), add supplement ridge lines and minutiae to mean fingerprint. (4) Post-processing: this step will help removing the noise of step 3.” Theory applied to fingerprint analysis ACE-V methodology is used to analyze fingerprints in many countries of the world including USA.

It consists of a 4-stage study of ridges that are visible. Stage 1 – an analysis of flow of ridges and visible patterns. Stage 2 – comparing two images of the finger's ridges, e.g. formation of shape at different intersecting points, Stage 3 – evaluating other characteristics including ridges, shape, width, and pore structure, if visible, and Stage 4 – verifying and endorsing the results elicited from the above processes. The outcome is subject to both objective (quantifiable) analysis of the prints and subjective explanations developed by the experts in identifying and matching fingerprints.

Earlier many jurisdictions used to rely on statistical evidence of positive relationship between the prints, however, they now mostly rely purely on experts’ verdicts. Faigman, D. (2008) quotes, “The court in United States v. Mitchell helpfully summarized the general understanding of the levels of detail considered by examiners at the analysis stage: [There are] three levels of increasing detail, designated as Level 1, Level 2, and Level 3. Level 1 detail is visible with the naked eye; it is the familiar pattern of loops, arches, and whorls.

Level 2 detail involves “ridge characteristics”—the patterns of islands, dots, and forks formed by the ridges as they begin and end and join and divide. The points where ridges terminate or bifurcate are often referred to as “Galton points,” whose eponym, Sir Francis Galton, first developed a taxonomy for these points. . . . Level 3 detail focuses on microscopic variations in the ridges themselves, such as the slight meanders of the ridges (the “ridge path”) and the locations of sweat pores.

” References Thai, L. & Tam, H. (2010). Fingerprint recognition using standardized fingerprint model, International Journal of Computer Science, 7(3), 11-17. Retrieved May 3, 2011, from http://www.scribd.com/doc/33313731/Fingerprint-recognition-using-standardized-fingerprint-model Faigman, D. (2008). Anecdotal Forensics, Phrenology, and Other Abject Lessons from the History of Science, Hastings Law Journal, 59, 979-1000. Retrieved May 3, 2011, from http://www.uchastings.edu/hlj/archive/vol59/Faigman_59-HLJ-979.pdf