By Claus Vielhauer (auth.)
Biometric consumer authentication strategies evoke a massive curiosity by means of technological know-how, and society. Scientists and builders consistently pursue expertise for automatic choice or affirmation of the id of matters in response to measurements of physiological or behavioral qualities of humans.
Biometric consumer Authentication for IT safety: From basics to Handwriting conveys common principals of passive (physiological trait like fingerprint, iris, face) and energetic (learned and knowledgeable habit like voice, handwriting and gait) biometric acceptance suggestions to the reader. This specialist ebook, in contrast to different guides during this region which be aware of passive schemes, displays a extra finished research of 1 specific lively biometric method: handwriting. facets that are completely mentioned contain sensor attribute dependency, assault eventualities, and the new release of cryptographic keys from handwriting.
Biometric person Authentication for IT safety: From basics to Handwriting is designed for a qualified viewers composed of practitioners and researchers in undefined. This quantity can be compatible for graduate-level scholars in machine technological know-how and electric engineering for the learn of biometrics within the spectrum starting from sign processing to IT security.
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Extra resources for Biometric User Authentication for it Security: From Fundamentals to Handwriting
However, significant improvements in recognition rates have been reported over the years, however most of them have been justified based on evaluations on proprietary test sets. Only recently, a first open contest has been performed based on 33 different algorithms; all of them tested by using one identical reference database [YCXG+2004]. Here, the best system has shown an EER of slightly above 2%, which appears to be rather low for an active biometrics. However, in this initial test, only the verification mode has been analyzed and the ROC along with EER have been published.
Distance measure based algorithms determine distances as measure of dissimilarity between reference and actual features. They perform a verification decision based on comparison to a given threshold value. Like in voice biometrics, statistical models are also used for the modality of handwriting, particularly HMM have been suggested widely. Again, such statistical models result directly in matching probabilities rather than differences in measurement. The third category of classification schemes uses neural networks for this purpose by training them with different types of features, along with identities of users during enrollment.
In order to do so, the system simply needs to be parameterized by the threshold value of T corresponding to desired false match probability on the ordinate of the error rate diagram. Higher FNMR are bothersome for authentic users, as they increase the probability of false rejections. Consequently, from an application point of view, this thresholding represents a trade-off between convenience and user acceptance on one side and security level on the other. 5 Biometric Signal Processing and Pattern Recognition The task of biometric user authentication requires methods from the scientific disciplines of signal processing and pattern recognition, as shown in the following Figure 2-7.