One of the major difficulties of facial recognition, is that you have to cope with the fact that a person’s appearance may change, such that the two images that are being compared differentiate too much from each other. Also environmental changes in pictures, like lightning, have to be taken into account, in order to have successful facial recognition. Thus from a picture of a face, as well as from a live face, some yet more abstract visual representation must be established which can mediate recognition despite the fact that in real life the same face will hardly ever form an identical image on successive occasions. Our ability to do this shows that we can derive structural codes for faces, which capture those aspects of the structure of a face essential to distinguish it from other faces[6]. One of the four most famous facial recognition methods is the Eigenface Method. This method focuses on the aspects of the face stimulus that are important for identification. This is done by decoding face images into significant local and global ‘features’[24]. Such features may or may not be directly related to our intuitive notion of face features such as the eyes, nose, lips and hair. Scientists Matthew Turk and Alex Pentland [24] developed a computer system for the eigenface approach which works as following: “In the language of information theory, we want to extract the relevant information in a face image, encode it as efficiently as possible, and compare one face encoding with a database of models encoded similarly.”[24] This all happens in the following initialization operations: 1) Acquire an initial set of face images, also called the training set.>