Fusion of face recognition method Research Paper

Fusion of face recognition method - Research Paper Example finger 3 details these matches considering Amber1 and Andy2 (dashed arrows points to some of the analogous features that may be distinguished from the eigenface).It was observed (from Figure 1 and 2) that Amber1 pass more distinguished features from the eigenface. Thus we can say that the eigenface closely and strongly resembles Amber1. Amber1s reconstructed images (shown in Figure 5 - include the step by step reconstruction of Amber1s face contained in folder ReconstructedPictures) supports this observation. beforehand finding the eigenfaces, however, we first need to collect a set of face images. These face images become our database of cognise faces. We will later determine whether or not an unknown face matches any of these known faces. completely face images must be the same size (in pixels), and for our purposes, they must be grayscale (shown in Figure 6), with value ranging from 0 to 255 (Krueger, J, et al, Obtainin g the Eigenface Basis).Eigenfaces are basically basis vectors for real faces. This can be related square to one of the well-nigh basic concepts in electrical engineering Fourier analysis. Fourier analysis discloses that a perfume of charge 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 item features of the face. The eigenface technique uses much more information by classifying faces based on general facial patterns. These patterns include, scarce are not limited to, the specific features of the f