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By Jacobs K., Keane M.

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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.

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