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Compatible with latest GS1 specification to encode, produce scannable QR Code images; .Related: Generate Barcode .NET Winforms how to, Excel Barcode Generation how to, .NET Winforms Barcode Generating SDK This results with the same effect on all images when computing the biometric feature information Features are calculated from a set of Nq images using different component analysis methods such as principal component analysis (PCA, also referred to as eigenface features) [12, 16] and Fisher linear discriminant (FLD) [17] p and q are S 1 vectors of the population and individual mean distributions, while p and q are S S matrices of the individual and population covariance matrices The feature decomposition process was conducted on 18 images of each of 16 persons, giving 288 total images For PCA and Fisher feature decompositions, 288 separate vectors were computed, and the most signi cant 100 features used for subsequent analysis Figures 231 and 232 illustrate PCA and FLD features, respectively From this, D(p q) is computed for each of 16 persons using Eq (23. 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The relatively small condition number of Sq indicates that no features are degenerate for PCA and FLD face recognition features. However, Sp is severely ill-conditioned. To overcome this ill-conditioning, we introduced a regularization scheme based on a mask [Eq. (23.12)] with a cutoff point L. This scheme is motivated by the diagonal structure of Sp , as shown in Figure 23.4. To ensure convergence, the mask size L is set to a value smaller than Np . We solve this singularity of Eq. (23.11) using a mask for Sp based on a parameter L. To further explore the effect of parameters L and Np , we arti cially reduce the Np by randomly eliminating some images from individuals. Results for D(p q) for PCA features for each person as a function of L are shown in Figure 23.5 for Np = 8, 12, 16, and 18. In these curves, we observe a hockey stick shape. 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