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All digits had a left-right structure as state transition model. 6 Opinion Fusion by Supervisor A more extensive presentation of the mathematical background of the model we used can be found in Biglin [3,2]. Basics of the Supervisor Algorithm We perform the following steps 1. e. e. Xij, Yj, and Pij with j up to n. The bias parameters will be computed for each expert by using all available persons in the training set. alj are computed according to (15), (16). tJ P~ . tJ n- 3 P~ . tJ (15) 47 2. (Authentication Phase) At this step, the supervisor is operational, meaning that the time instant is always n + 1 and that the supervisor has access to expert opinions Xi,n+l, and Pi,n+l, but not access to the true authentication scores, Yn+l' The expert opinions are normalised yieldingM' , and V' according to (17).

1. 1.. 6~~O~ . 9~~ tht9$hOld value Fig. 7. Total error rates according to the threshold for the rigid matching and the elastic graph matching, with A = 2. 5 threshold are shown for several values of ), in Table 1. The Equal Error Rate (EER), defined as the point where FA = FR, is also shown. There is a transition from elastic to rigid matching. 0. For larger values of A, the performance degrades. The elastic graph matching improved the 40 rigid graph matching, which can be observed by inspecting Figure 7.

Jolicoeur, P. (1985). The time to name disoriented natural objects. Memory & Cognition, 13, 289-303. , & McDermott, J. (1996). fMRI in individual subjects reveals loci in extrastriate cortex differentially sensitive to faces and· objects. Investigative Ophthalmology & Visual Science, 37, 193. , & Cooper, E. E. (1994). To what extent can the recognition of unfamiliar faces be accounted for by a representation of the direct output of simple cells. Investigative Ophthalmology & Visual Science, 35, 1626.

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