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By Ted Dunstone

Biometric approach and knowledge research: layout, review, and information Mining brings jointly features of facts and laptop studying to supply a accomplished advisor to guage, interpret and comprehend biometric facts. This expert booklet obviously results in issues together with info mining and prediction, greatly utilized to different fields yet no longer conscientiously to biometrics.

This quantity locations an emphasis at the a number of functionality measures to be had for biometric platforms, what they suggest, and after they should still and shouldn't be utilized. The assessment recommendations are offered carefully, even if are regularly observed by way of intuitive reasons that express the essence of the statistical options in a basic manner.

Designed for a qualified viewers composed of practitioners and researchers in undefined, Biometric procedure and knowledge research: layout, review, and information Mining can be compatible as a reference for advanced-level scholars in laptop technology and engineering.

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Extra info for Biometric System and Data Analysis: Design, Evaluation, and Data Mining

Sample text

36 2 Biometric Matching Basics Fig. 5 Card door access. A camera at the door takes a photo and compares it to the facial template stored on the card to determine whether or not to allow access. 1 Matching Data To begin, the 10 access cards are handed out - some to the genuine users and some to people who are not the correct card holders. Each person in the test presents their card, labeled (A-J), to the door and a match score is generated depending on the similarity of the enrollment stored on the card and images collected from camera.

This score is the fundamental building block of the analysis techniques presented in this book. For verification, the comparison score is used to make a decision about accepting the person as genuine or rejecting them as an impostor. Alternatively, during identification the match is conducted against two or more enrolled people to produce a candidate list of possible genuine matches. The decision policy of whether to accept or reject them should be based on a sound understanding of the true likelihood of a mistake, and is discussed in detail in Chap.

00 20 40 60 80 100 Score Fig. 2 A match score histogram for the hypothetical laptop fingerprint system. The black bars represent the genuine distribution (the range and frequency of genuine scores) and the white bars represent the impostor distribution (the range and frequency of impostor scores). as well as other animal types and detection methods, is the subject of discussion in Chap. 8. g. g. g. g. g. Weak templates (lambs): a) Light Workers with fingerprints that have been worn fingerprints with lots of spurious minutiae that off through using abrasive substances are unusually easy to score high against.

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