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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. Figure 1: Images of the training set [26] Figure 2: Eigenfaces of the training set [26] 2) 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 charac

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