By R. M. Duwairi, N. J. Fiddian, W. A. Gray (auth.), Ron Morrison, Jessie Kennedy (eds.)
This e-book constitutes the refereed complaints of the 14th British nationwide convention on Databases, BNCOD 14, held in Edinburgh, uk, in July 1996.
The thirteen revised papers awarded within the publication in complete model including invited talks and 3 commercial abstracts have been chosen from a complete of forty seven submissions related to authors from 21 international locations. The papers integrated are written by means of researchers and pros from academia and undefined; the amount is equipped in topical sections on object-oriented databases, integrity matters, database functionality and optimization, and database languages.
Read Online or Download Advances in Databases: 14th British National Conference on Databases, BNCOD 14 Edinburgh, United Kingdom, July 3–5, 1996 Proceedings PDF
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Additional info for Advances in Databases: 14th British National Conference on Databases, BNCOD 14 Edinburgh, United Kingdom, July 3–5, 1996 Proceedings
Finally, the same approach can be applied to morphing between faces of diﬀerent subjects. 1 Virtual Makeup Our ﬁrst application is the “virtual makeup” – expression-invariant mapping of a single texture image onto a 3D video of an animated face. We ﬁrst draw the texture (represented as the ﬁeld α0 : S0 → R3 , consisting of the R, G and B channels) on the reference frame S0 . Next, using the correspondences, we map the texture onto the rest of the frames in the 3D video. A scheme of the procedure is depicted in Figure 2.
As in J. Daugman method, the summated focus value by convolution kernel is passed through a compressive non-linearity of the form: f(x) = 100·x2/(x2+c2), which can make a normalized focus score belong to the range of 0 to 100 . Here, x is the total power spectrum measured by the (5 5) pixels convolution kernel as shown in Fig. 2. ｘ ｘ Fig. 2. The proposed (5×5) convolution kernel to measure focus score In order to compare the performance of the Daugman’s  and Wei’s  convolution kernel to that of the proposed (5 5) pixels kernel, we inspected the 2-D power spectrum of each method.
The FAR and the FRR 4 Conclusions For higher security level of iris recognition, the importance for detecting fake iris is much highlighted recently. In this paper, we propose the new method of checking the hippus movement (the dilation/contraction of pupil size) and the change of iris code in local iris area by visible light. 33% and the FAR (Error rate of accepting forgery iris as live one) is almost 0%. To enhance the performance of our algorithm, we should have more field tests and consider more countermeasures against various situations and counterfeit samples in future.
Advances in Databases: 14th British National Conference on Databases, BNCOD 14 Edinburgh, United Kingdom, July 3–5, 1996 Proceedings by R. M. Duwairi, N. J. Fiddian, W. A. Gray (auth.), Ron Morrison, Jessie Kennedy (eds.)
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