By Ted Dunstone
Biometric procedure and knowledge research: layout, assessment, and information Mining brings jointly features of records and desktop studying to supply a complete advisor to guage, interpret and comprehend biometric info. This expert ebook evidently results in issues together with facts mining and prediction, greatly utilized to different fields yet no longer conscientiously to biometrics.
This quantity areas an emphasis at the a variety of functionality measures on hand for biometric platforms, what they suggest, and after they should still and shouldn't be utilized. The evaluate concepts are provided conscientiously, besides the fact that are consistently observed via intuitive motives that exhibit the essence of the statistical innovations in a basic manner.
Designed for a certified viewers composed of practitioners and researchers in undefined, Biometric method and knowledge research: layout, assessment, and information Mining can be compatible as a reference for advanced-level scholars in laptop technology and engineering.
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Biometric process and knowledge research: layout, evaluate, and information Mining brings jointly features of records and laptop studying to supply a finished advisor to judge, interpret and comprehend biometric info. This specialist publication obviously ends up in issues together with information mining and prediction, largely utilized to different fields yet no longer conscientiously to biometrics.
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Additional info for Biometric System and Data Analysis: Design, Evaluation, and Data Mining
However, when the passport is used at an automated immigration gate, a false match would mean that the user was in possession of another person’s passport. This also illustrates that the types of vulnerability are different for the two scenarios. For positive identification the impostor must try to look similar to the true passport owner. However, for negative identification an ‘impostor’ must try not to look like themself. 2 Common Biometric Processes The matching process of a biometric can be simplified into two phases: the capture and comparison of the biometric sample, and a decision as to whether to accept or reject the input as authentic.
For some people a particular algorithm may find recognition inherently hard because the biometric features used by the algorithm are missing, or difficult to detect. Users who fall into this category have traditionally been called goats. For example, our laptop user may have difficulty because they are always using hand cream, which can cause matching difficulties as some sensors are disturbed by the extra coating on the fingers. Additionally, if our user is from a demographic that has smaller than average fingers, the smaller features could cause matching problems.
Because of variations in the way a biometric sample is captured, two templates from the same biometric will never be identical. This is the origin of the probabilistic nature of biometrics, as the matching process can only give a decision confidence, not an absolute assurance (see Chap. 2 for more details). 5 Biometric Data 15 (a) (b) (c) (d) (e) (f) (g) (h) (i) Fig. 1 Examples of the diversity of biometric samples: (a) fingerprint , (b) face , (c) iris, (d) vein  (e) voice (spectrogram) , (f) infrared face  (g) 3D facial geometry , (h) typing dynamics, and (i) DNA.