Titlebook: Automatic Fingerprint Recognition Systems; Nalini Ratha,Ruud Bolle Book 2004 Springer Science+Business Media New York 2004 algorithms.biom

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Generation of Synthetic Fingerprint Image Databases,yed to deal with the distortion resulting by pressing a finger against the flat surface of an online sensor. SFINGE produ ces very realistic fingerprint images, which can be used for training, testing, and optimizing fingerprint recognition algorithms. This approach has been validated by proving tha

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https://doi.org/10.1007/0-387-30528-9cement of images is discussed. A detailed evaluation of the enhancement algorithm in comparison with that contained in a working Automatic Fingerprint Recognition (AFR) system is given..A mathematical model of the fingerprint local ridge orientation as a function of position within the image is deve

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Lipschitz and Continuous Optimizationn of different matching algorithms can improve the overall matching performance. Fingerprint matching is a challenging pr oblem due to the large intraclass variation between different impressions of the same finger, noise in the fingerprint images, and small interclass variations in fingerprint imag

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Successive Approximation Methodsds is approximately 90 and is 10. times faster than minutiae matching computation. This preselection scheme en ables the realization of fingerprint identification systems of high computational performance.

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https://doi.org/10.1007/978-3-662-03199-5res measurement of missed detection and false-alarm error rates. Two parameters are derived from these erro r rates, . and .. Reliability is the probability that the system will report a matching fingerprint given that one is in the database. Selectivity is the average number of false candidates per

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