Figure 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 have more distinguished features from the eigenface. Thus we can say that the eigenface closely and strongly resembles Amber1. Amber1's reconstructed images (shown in Figure 5 - include the step by step reconstruction of Amber1's face contained in folder "ReconstructedPictures") supports this observation.
Before finding the eigenfaces, however, 'we first need 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 any 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 the Eigenface Basis").
Eigenfaces are 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 person's face". (Krueger, J, et al, "Obtaining the Eigenface Basis")
According 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