Fusion of face recognition method Research Paper

Fusion of face recognition method - Research Paper ExampleFigure 3 details these matches considering Amber1 and Andy2 (dashed arrows points to some of the kindred features that may be distinguished from the eigenface).It was observed (from Figure 1 and 2) that Amber1 have more than distinguished features from the eigenface. Thus we can say that the eigenface closely and strongly resembles Amber1. Amber1s hypothesise images (shown in Figure 5 - include the step by step manufactureion of Amber1s face contained in folder ReconstructedPictures) supports this observation.Before finding the eigenfaces, however, we first hire to collect a set of face images. These face images become our database of known faces. We will later determine whether or not an unknown face matches whatsoever of these known faces. All face images must be the same size (in pixels), and for our purposes, they must be grayscale (shown in Figure 6), with values ranging from 0 to 255 (Krueger, J, et al, Obtaining t he Eigenface Basis).Eigenfaces argon basically basis vectors for real faces. This can be related straightforwardly to one of the most basic concepts in electrical engineering Fourier analysis. Fourier analysis discloses that a sum of weighted sinusoids at differing frequencies can recompose a signal perfectly In the same manner a sum of weighted eigenfaces can seamlessly reconstruct a specific persons face. (Krueger, J, et al, Obtaining the Eigenface Basis)CONCLUSIONAccording to Krueger, J. & et al, the eigenface technique is a powerful yet simple solution to the face recognition dilemma. In fact, it is really the most intuitive way to classify a face. As we have shown, old techniques focused on particular features of the face. The eigenface technique uses much more information by classifying faces based on general facial patterns. These patterns include, but are not limited to, the specific features of the f