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4. 5%. Although this does not comply with the protocol exactly, it is still posible to compare performance. In table 4, TB and IDIAP (2000) used Gabor-based approaches. 2. Experiment 2 In the second test, we compared the full selection method (layers 1 and 2) against a downsampled rectangular grid, in which final nodes were selected according to the accuracy-based method explained in section 3. 1. Again, as shown in table 2, our technique outperformed the rectangular grid approach. From tables 1 and 2, it is important to notice that the use of layer 2 also produced a clear improvement in the authentication rates for the rectangular grid.
In this paper, we present a bi-layer node selection approach. First, a client-dependent facial structure is exploited to select possible discriminative locations and, in the second stage, a subset of these positions is kept based on their individual classification accuracies. The hypothesis in both stages of discriminative point selection is that every user should keep the most representative features of himself and discard those shared with other users. The paper is organized as follows: Section 2 presents the shapedriven selection, feature extraction and matching stage, while section 3 explains the second layer of node selection.
Unfortunately, the obtained coordinates do not lie on the Euclidean space , which makes this representation not very useful. In order to upgrade the representation, extra information is required. This extra information can be obtained either from the camera position or from the camera intrinsic parameters. The latter can be calculated either from the use of special calibration patterns or from the images of our input set. The procedure of utilizing the images that we have in order to calculate the camera intrinsic parameters is called self calibration as opposed to calibration where some specific calibration patterns are used in order to calculate the camera calibration matrix.