Handbook of Fingerprint RecognitionSpringer Science & Business Media, 6 апр. 2006 г. - Всего страниц: 351 Overview Biometric recognition refers to the use of distinctive physiological and behavioral character- tics (e. g. , fingerprints, face, hang geometry, iris, gait, signature), called biometric identifiers or simply biometrics, for automatically recognizing a person. Questions such as “Is this person authorized to enter the facility?”, “Is this individual entitled to access the privileged infor- tion?”, and “Did this person previously apply for a job?” are routinely asked in a variety of organizations in both public and private sectors. Because biometric identifiers cannot be easily misplaced, forged, or shared, they are considered more reliable for person recognition than traditional token- (e. g. , keys) or knowledge- (e. g. , password) based methods. Biometric recognition can provide better security, higher efficiency, and increased user convenience. It is for these reasons that biometric systems are being either increasingly deployed or evaluated in a large number of government (e. g. , welfare disbursement, national ID card, issuing of driver’s license) and civilian (e. g. , computer network logon, automatic teller machine, cellular phone, Web access, smartcard) applications. A number of biometric technologies have been developed and several of them are being used in a variety of applications. Among these, fingerprints, face, iris, speech, and hand - ometry are the ones that are most commonly used. Each biometric has its strengths and we- nesses and the choice of a particular biometric typically depends on the requirements of an application. |
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Fingerprint Sensing | 53 |
Fingerprint Analysis and Representation | 83 |
Fingerprint Matching | 131 |
Fingerprint Classification and Indexing 173 | 172 |
Synthetic Fingerprint Generation | 203 |
Multimodal Biometric Systems | 233 |
Fingerprint Individuality 257 | 256 |
Securing Fingerprint Systems | 281 |
Bibliography | 311 |
341 | |
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Handbook of Fingerprint Recognition Davide Maltoni,Dario Maio,Anil K Jain Недоступно для просмотра - 2014 |
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accuracy alignment applications approaches attack automatic fingerprint Bazen binary binary image biometric identifiers brute force attack Candela Cappelli classifiers combination computed Conf correlation corresponding database detection distortion distribution encryption estimated example fake finger false match false non-match feature extraction Figure fingerprint area fingerprint classification fingerprint features fingerprint images fingerprint individuality fingerprint matching fingerprint recognition system fingerprint scanner fingerprint verification fingerprint-based FNMR Gabor filters gerprint gray-scale hand geometry IEEE impostor input fingerprint Jain large number latent fingerprint live-scan Maio Maio and Maltoni matcher matching algorithms matching score method minutiae matching module neural network NIST optical orientation image pair Pankanti parameters password Pattern Recognition performance pixels Prabhakar pre-alignment Proc proposed public-key cryptography quality fingerprint Ratha regions representation ridge count ridge line ridge orientation SFINGE singularities smartcard synthetic fingerprint techniques tion transformation Trojan horse types verification system whorl