Sample Solution

Calculate the eigenfaces from the training set, keeping only the M images that correspond to the highest eigenvalues. These M images define the face space. As new faces are experienced, the eigenfaces can be updated or recalculated.
3) Calculate the corresponding distribution in M-dimensional weight space for each known individual, by projecting their face images onto the ‘face space’.

After the initialization operations, there are carried out more operations in order to recognize new face images.
4) Calculate a set of weights based on the input image and the M eigenfaces by projecting the input image onto each of the eigenfaces.
5) Determine if the image is a face at all by checking to see if the image is sufficiently close to ‘face space’.
6) If it is a face, classify the weight pattern as either a known person or as unknown.
7) (Optional) Update the eigenfaces and/or weight patterns.
8) (Optional) If the same unknown face is seen several times, calculate its characteristic weight pattern and incorporate into the known faces[24].

As mentioned earlier, there is a long list of methods that can be used for facial recognition. Four of them, i.e Eigenface Method, Correlation Method, Fisherface Method and the Linear Subspaces Method, are the most favorite. Below here, you can find the error rates of those four methods, considered