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This paper proposes a weighted power series model for face verification scores fusion.
The peaks on the score surface, judiciously chosen in the light of a prior model for face geometry form the landmarks.
Section 1.3 presents the proposed methodology step by step, including a skin model for face detection using Gabor features, PC features, and entropy-weighted Gabor phase congruency (EWGP).
Covariates that were a priori known to be associated with the outcome of interest were forced into the model for face validity regardless of level of significance.
Gobbini and Haxby (2007) have proposed a neural model for face perception in which the anterior paracingulate, posterior superior temporal sulcus, and temporal-parietal junction serve in the retrieval of personal traits, intentions, attitudes, and mental states of familiar individuals.
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The approach has been successfully applied to a rigid viscoplastic Taylor-type model for face-centered cubic polycrystals.
A dislocation density based constitutive model for face-centred cubic crystals is introduced and implemented into a crystal plasticity finite element framework.
Based on the embedded atom method we have studied dislocation bypassing of nanophases in a model for face-centered cubic (fcc) alloys.
A new crystal plasticity model for face-centered cubic metals exhibiting deformation twinning is presented and assessed based on a comprehensive and original experimental dataset for twinning-induced plasticity (TWIP) steel.
Deposition fractions were also predicted by the improved model for faces of single and inline buildings.
Learning the multiclass SVM models: for face databases, we learn four multi-class SVM models using the images from the four benchmark databases (one multiclass SVM per benchmark database illustrated by SVMeach) and one multiclass SVM model containing examples from the four benchmark databases (illustrated by SVMall).
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